On November 17, 2022, the Culture section of The Economist published in the article «The classical-music world is grappling with accessibility» some views on the «relevance» and «accessibility» of music, because these could be counterproductive inasmuch as «the clamour for classical music to be more approachable and relevant risks drowning out the music itself». The democratization of music could represent an unexplored and risky path. One of the biggest obstacles is the persistent «thought» that classical music is «intimidating», even «an art form perceived to exemplify snobbery, stuffiness, and racial privilege». These ways of "thought" about music could be analyzed by different lines of research in different disciplines, and give rise to extensive debates and whole books. Each point of view could be argued truthfully. It would be like having a box of chocolates and carefully analyzing its contents by selecting a feature such as design, brand, taste, nutrients, price, costs, market position, et cetera, but this feature would be part of reality. Analyzing it from a universal perspective would be a bit risky.
Introduction
The piece of reality studied in this post corresponds to a sample of 974 free classical music broadcasts from more than 20 countries, easily accessible to any internet user and available on 26 websites and 3 social media platforms (YouTube, Facebook and Vkontakte) during the time span from May 19, 2022 to December 31, 2022. It has been shown that the «thought» that classical music is an "exclusive" art form does not apply to live broadcasts, and no evidence has been found that they could drown out the music. This blog arises out of necessity, so as not to miss live broadcasts of our favourite orchestras. Thank you to the generosity of those who make music and share their programmes, this calendar exists and we will hardly find days without scheduled events. This is to celebrate.
Methodology
The main hypothesis was that the most widely broadcasted works on the Internet do not pose a threat to the music itself and are not "exclusive" to a particular population group. Based on the results of the descriptive statistical analysis, the most popular composers and works by orchestras were counted. Now, it is time to examine the data using Social Network Analysis (SNA) and geospatial tools. The first part corresponds to SNA for works and orchestras; the second to spatial analysis of interpolation by city and influence by country.
The first step was to find works performed on at least three occasions [Figure 1], then the orchestras that played them, and finally the city where they were played -or broadcast-. It is worth mentioning that the selection of works was reduced only to symphonies and concertos with orchestral accompaniment.
Figure 1. Methodology step by step
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In the first part of this study, «Data analysis of classical music broadcasts in 2022 (1/2) -Descriptive section-», it was found that the composers most most frequently played by orchestras are those that the audience could perhaps quickly identify, such as Ludwig Van Beethoven, Wolfgang Amadeus Mozart, Pyotr Ilyich Tchaikovsky, Fryderyk Chopin, Maurice Ravel, Sergei Rachmaninoff, Dmitri Shostakovich, Johann Sebastian Bach, and others [Table 1. Most performed composers in 2022]. However, the analysis also included the search for the works most frequently performed by the orchestras, which included authors such as (in descending order) AntonÃn Dvorak, Frédéric Chopin, Gustav Mahler, Dimitri Shostakovich, Felix Mendelssohn, Johannes Brahms, et cetera (Figures 4 and 5).
This behavior could be explained in multiple ways; from the perspective of the economy as productive units, from sociology by The Matthew Effect explained by Robert K. Merton and even from the historical point of view by the «experience» exposed by Reinhart Koselleck. There are a variety of possibilities that could explain this selection of works and composers by the orchestras, but let's start with these three proposals.
Results
Social Network Analysis (SNA) [overview]
The SNA allows us to identify the strength of links at micro and macro levels between individuals, entities, groups, et cetera with a greater or lesser number of connections between them, it is possible to find the strongest or weakest members, as well as determine that «the stronger the tie connecting two individuals, the more similar they are, in various ways» (Granovetter, 1973, p. 1362). The strong and weak relationships between orchestras (actors) and performed works (nodes) were analyzed, however, it was not enough, so the study of the position of the actors, their density, their power and the formation of clusters were examined (Burt, 2004, pp. 351-355). In addition, the evidence of homophily in the network -of the actors that are similar to each other- was searched (McPherson et al., 2001, pp. 429, 435).
It was necessary to build a directed rectangular matrix (one mode) of 92 rows (orchestras) and 51 columns (works). The objective was to classify each work performed by orchestra. The labels {O1, O2, ..., O92} represent orchestras and {W1, W2, ..., W51} were assigned to works [base matrix]. The graphic representation corresponds to the centrality measure of the Eigenvector [Figure 2]. At first glance, the node W2 stands out (Symphony No. 9 in E minor by AntonÃn Dvorak, Op. 95 “From the New World”) as well as the actor O70 (Academic Symphony Orchestra of the North Caucasian Philharmonic), however, we could get these results from the post above. Now, it is about the graph representation among the actors most connected with the most popular works, the sizes of the squares and circles correspond to the centrality of the Eigenvector. If we divide it into groups (K-cores) then we can find the greatest centrality in 26 orchestras with the greatest connection with 35 works, these are in the pink area of the Figure 2.
Figure 2. Orchestras (actors) and Works (nodes)
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If this were a multivariable model, the independent variables could be found in the common elements that lead orchestras to choose AntonÃn Dvořák's 9th symphony as the work they perform most often; each work would then be a dependent variable. But that would be a different kind of research. For now, let's focus on social network analysis and transform the directed asymmetric matrix [base matrix] into two undirected symmetric matrices to understand the role of each actor and node in Figure 2 above.
Who is who among the works?
Initially, 974 broadcasts were counted, but eleven per cent of these did not include a description of the works to be performed during the programme, in such a way that only the content of 869 programmes was analyzed, from which the names of 44 composers were obtained, whose works were performed nine or more times. The search continued until the works that were performed in at least three different programmes [Figure 2]. For example, works by Camille Saint-Saëns were presented 26 times but none were repeated three times, whereas AntonÃn Dvořák's works obtained 40 coincidences in the programmes and three of his works were among the most performed [Symphony No. 9 in E Minor, Op. 95 "From the New World" (14), Cello Concerto B Minor Op.104 (7) and Violin Concerto in A Minor, Op. 53 (3)].
Figure 3. Composers and number of works performed
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Instead, Ludwig van Beethoven scored 97 matches within programmes and nine of his works were repeated 3 to 7 times, [Symphony No. 9 in D Minor, Op. 125 [with / without chorale] (7); Piano Concerto No. 3 in C minor, Op. 37 (6); Symphony No. 7 in A Major, Op. 92 (4); Symphony No. 6 in F Major, Op. 68 "Pastoral" (4); Symphony No. 5 in C minor, Op. 67 (4); Symphony No. 3 in E flat major, Op. 55 "Heroic" (4); Piano Concerto No. 5, in E flat major, Op. 73 "Emperor" (4); Piano Concerto No. 1 in C Major, Op. 15 (3); and Concerto for violin, cello and piano in C major, op. 56 (3)].
Let us remember that the selected works were those that were performed at least three times {W1, W2,...,W51} and of these, the orchestras that performed them {O1, O2,...,O92} were found. This matrix was symmetrized for the works, obtaining a matrix of 51 columns and 51 rows in order to analyze the position of each work in the network, for which the basic measures of centrality of Degree, Betweenness and Closeness were analyzed to compare the importance of each node (Freeman, 1979). The Degree allows for knowing the number of nodes directly connected to a node. Betweenness measures the intermediation frequency of a node with others, it works as a bridge. The Closeness allows us to measure the capacity of a node to reach the others. The centrality of the Eigenvector (Wolitzky, 2022) allows us to calculate the most central works, as long as they are connected to other central ones. The centrality of these nodes is derived from the centrality of the nodes that point to them; these are the most prestigious works.
Another indicator of centrality is the well-known Bonacich, when a positive value is assigned to it (+β), the centrality measure obtained connects directly or indirectly to each work, these are the works with the greatest influence on the network (Bonacich, 1987); on the other hand, when a negative value is assigned to it (-β), an indicator of power is obtained, which would be those works that are also directly connected with unpopular works. The works farthest away in the network depend on popular works to dispatch information to the network.
According to the centrality measures [Table 1] it can be observed that the works most connected (Degree and Eigenvector) to the network: Symphony No. 9 in E minor op. 95 «From the New World» (W2), Cello Concerto in B minor Op.104 by AntonÃn Dvorak (W1) and Piano Concerto in A minor Op. 16 (W3) by Edvard Grieg. The closest works with the shortest route to reach the other works: Ludwig van Beethoven's Concerto for violin, cello and piano in C major, Op. 56 (W28), Sergei Prokofiev's Piano Concerto No. 3 in C Major Op. 26 (W44) and Felix Mendelssohn's Piano Concerto No. 1 in G minor, Op. 25 (W11). Intermediate works that can link peripheral works: AntonÃn Dvorak's Cello Concerto in B minor Op. 104 (W1), AntonÃn Dvorak's Symphony No. 9 in E minor, Op. 95 "From the New World" (W2) and Felix Mendelssohn's Violin Concerto in E (W12).
Table 1. Measures of centrality and power (Works)
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The most influential works [Figure 4]: Johannes Brahms' Symphony No. 2 in D Major, Op. 73 (W24), Felix Mendelssohn's Violin Concerto in E (W12) and Johannes Brahms' Piano Concerto No 2 in B Flat Major, Op. 83 (W23) because these could be considered the best friends of the nodes with the highest connectivity in the network. These are the works best related to the most connected works in the entire network. The most powerful works: AntonÃn Dvorak's Symphony No. 9 in E Minor, Op. 95 "From the New World" (W2), Edvard Grieg's Piano Concerto in A Minor op. 16 (W9) and Felix Mendelssohn's Violin Concerto in E (W12) because less networked works depend on them. These are the works that generate dependency on the works further away from the network to connect to it.
Figure 4. Influence and power of the works
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How are the programmes integrated? What is behind the decisions in selecting the works to be performed? For now, let us stay with the measurements of geodesic distance [Figure 5]. This algorithm makes it possible to measure the number of relationships along the shortest path between one work and another. The 30 percent of the works have a length of 1; 62 percent have a length of 2; 8 percent have a length of 3; and 0.1 percent have a length of 4. This is a moderately dense network in which information can flow quickly and is accessible to the actors; any message emanating from this network reaches everyone.
Figure 5. Distance from one work to another
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What would be the most efficient way for each actor to get a message across? To obtain these data [Figure 5], the algorithm was calculated to determine the number of geodesic routes, and it was found that in addition to having short distances, some works also have multiple efficient paths, but how weak or strong are these? The information sent by a performance is strong in relation to how many connections the message can travel to other nodes. The works that send the greatest flow of information are AntonÃn Dvorak's Cello Concerto B Minor Op. 104 (W1), AntonÃn Dvorak's Symphony No. 9 in E Minor, Op. 95 "From the New World" (W2) and Edvard Grieg's Piano Concerto in A Minor op. 16 (W9). The mode was 15 maximum connections and the average was 14.
Orchestras
For the analysis of the actors, it was necessary to obtain the adjacent matrix of the rows of the original matrix [base matrix], forming a 2-mode matrix. The orchestras that performed the most works (from the selection of 51 works -in descending order-) were the Safonov Academic Symphony Orchestra of the North Caucasus Philharmonic (O70, 12), St. Petersburg Symphony Orchestra (O75, 11), San Francisco Symphony Orchestra (O72, 10) and St. Petersburg Philharmonic Orchestra (O77, 10). As can be seen in Figure 2 these orchestras have the largest number of connections with the works (nodes), but there are a large amount of information and relationships to be discovered within the network.
The internal consistency of the network was calculated using Cronbach's alpha (α =0.886) and the actors were correlated with each other. The most closely related orchestras were found: Gwacheon City Symphony Orchestra (O25) with Cheonan Philharmonic Orchestra (011) at 0.903; Minas Gerais Philharmonic Orchestra (O37) with Hungarian National Philharmonic (O30) at 0.920; Munich Philharmonic (O40) with Heliopolis Symphonic Orchestra (O28) at 0.838. This information, although accurate, is not complete, so we must now show the relationships within this square matrix of 92 rows and 92 columns.
If we group the orchestras into cliques (Wasserman & Faust, 1994) or subgroups [Figure 6] that are more closely connected, we find that the St. Petersburg Symphony Orchestra (O75) and the San Francisco Symphony Orchestra (071) have the longest path that would allow them to connect with the other orchestras, while the Royal College of Music (RCM) Symphony Orchestra (O61) and the Gunpo Prime Philharmonic Orchestra (O24) are less closely connected with the rest. The clique with the shortest path to the previous one is made up of the Youth Symphony Orchestra of Ukraine (O92) and BBC Philharmonic (O5).
Figure 6. Orchestras grouped in cliques
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The analysis of cohesive subgroups with high density was used, 439 cliques were found, these are groups that include some intermediary orchestras, but to find a greater cohesion, 219 clans were then found formed by the orchestras that are directly connected to each other, that is, without having a friend that connects to the others, because all orchestras must be connected to each other, in this way the orchestras that keep the leadership within the network would be the San Francisco Symphony Orchestra (O71) and the St. Petersburg Symphony Orchestra (O74). It should be noted that this analysis is rarely used in empirical research, which is why structural similarity analysis was used.
Similar orchestra groups are those in which each member is similarly connected to other orchestras, groups that have the same connections to the same orchestras (Burt, 1976), i.e., they behave similarly within the network [Figure 7]: [1] Chineke! Orchestra (014) and Victoria Symphony Orchestra (O86); [2] MGSAO (O36), Oslo Philharmonic (O59) and Sverdlovsk Philharmonic (077); [3] Vienna Radio Symphony Orchestra (O87) and Orquesta Estable del Teatro (O54); [4] Rotterdams Philharmonisch Orkest (O62) and National Symphony Orchestra (O41); [5] Chicago Symphony Orchestra (O12) and Chineke! Junior Orchestra (O13); [6] Medical Orchestra (034) and Russian National Philharmonic Orchestra (O68); [7] National Symphony Orchestra of the Republic of Belarus (O43) and Gothenburg Symphony Orchestra (O23); [8] BBC National Orchestra of Wales (O4) and Tula Philharmonic Symphony Orchestra (084); [9] Bergen Philharmonic Orchestra (O8), RAI National Symphony Orchestra (O60) and London Symphony Orchestra (O32); [10] Wiener Philharmoniker (091) and Cincinnati Symphony Orchestra (O15); [11] St. Petersburg State Rimsky-Korsakov Symphony Orchestra (O81) and Gyeonggi Philharmonic Orchestra (O27); [12] Ukranian Freedom Orchestra (O85) and Sinfonia Varsovia (O73); [13] Tatarstan Symphony Orchestra (082) and State Symphony Orchestra "New Russia" (O76); [14] Cheonan Philharmonic Orchestra (O11) and Tchaikovsky Symphony Orchestra (O83); [15] Melbourne Symphony Orchestra (O35), Royal Northern Sinfonia (O64), Heliópolis Symphonic Orchestra (O28), Youth Symphony Orchestra of Ukraine (O92) and Nuremberg Symphony Orchestra (O46).
Figure 7. Structural similarity
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In addition to the application of the algorithms for structural similarity, it should be noted that for coincidences, the result was the same for the profiles with the greatest similarity mentioned in the previous paragraph. The study of the groups that could be formed in this network required the analysis of Cliques (439) in which the groups formed can include an orchestra that acts as an intermediary within the group; through the analysis of Clans (219), it was necessary that all the orchestras are connected, eliminating the figure “friend of a friend”. However, to find the orchestras that have «the same links with the same actors» 15 groups with structural similarity were found.
Let's start by analyzing the Centrality of our Network, I also found a very practical paper by Junlong & Luo Yu (2017) that can be easily consulted. Now, let's see which of our actors are the most central in the network [Figure 8 and Table 2]. The Degree of centrality indicates the number of edges attached to each node; this is the number of actors that are connected to an orchestra. By measuring the Degree we can determine that the orchestras that are well connected have a direct influence: Safonov Academic Symphony Orchestra of the North Caucasus Philharmonic (O70), St. Petersburg Symphony Orchestra (072), St. Petersburg Philharmonic Orchestra (075), San Francisco Symphony (O71), hr- Sinfonieorchester (O29), Orquestra Sinfônica do Estado de São Paulo OSESP (O58) and BBC Symphony Orchestra (O7). On the contrary, the furthest away: Chicago Symphony Orchestra (O12), Chineke! Junior Orchestra (O13), European Union Youth Orchestra (O20), George Enescu Philharmonic Orchestra (O22), Gunpo Prime Philharmonic Orchestra (O24) and Royal College of Music (RCM) Symphony Orchestra (O61).
Figure 8. Proportions Degree and Eigenvalue centralities
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How far apart are orchestras from everyone else? The orchestras that can catch up with all the other actors faster (Closeness), in descending order: St. Petersburg Symphony Orchestra (O74), Safonov Academic Symphony orchestra of the North Caucasus Philharmonic (O70), Orquestra Sinfônica do Estado de São Paulo OSESP (O58), San Francisco Symphony (071), St. Petersburg Philharmonic Orchestra (O75), hr-Sinfonieorchester (029) and BBC Symphony Orchestra (O7). While the furthest away, in descending order: George Enescu Philharmonic Orchestra (O22), Orquesta Estable del Teatro (O54) and Vienna Radio Symphony Orchestra (O87), Russian National Orchestra (O68) and Gunpo Prime Philharmonic Orchestra (O24).
Now let's see who mediates information within the network, through the Betweenness measure: St. Petersburg Symphony Orchestra (O74), St. Petersburg Philharmonic Orchestra (O75), San Francisco Symphony (O71), BBC Symphony Orchestra (O7) and Orchestra Sinfônica do Estado de São Paulo OSESP (O58). These are the orchestras with the greatest mediation over the flow of information on the network. It should be mentioned that 45 orchestras that represent 49 percent of the total maintain a zero betweenness frequency, these cannot become a non-direct link. It should be noted that the betweenness measure does not provide for input or output addressing for each link.
Table 2. Centrality measures (Orchestras)
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Finding the central actors of this network means identifying the orchestras that have the highest density (Degree), closest (Closeness) and the most connected (Betweenness). An attempt was even made to identify them by similarity through Cliques, Clans and Structure. The objective of doing all this was to find the most important, influential and prestigious orchestras in the network.
Now, it is time to analyze the Eigenvector centrality measurement (Wolitzky, 2022), which calculates not only the centrality of the connections but also the centrality of the vectors. It is about identifying the most central orchestras, provided that they are connected to other centrals, that is, a proportion of the sum of scores of their neighbors. In the directed graph of Figure 2, the largest nodes were obtained based on this analysis. The centrality of these nodes is derived from the centrality of the nodes that point to them, positioning themselves as the most important and prestigious nodes. According to this indicator, we can identify the central orchestras: Safonov Academic Symphony Orchestra of the North Caucasus Philharmonic (O70), St. Petersburg Philharmonic Orchestra (O74), St. Petersburg Symphony Orchestra (O75), Deutsche Radio Philharmonie Saarbrücken Kaiserslautern (O19), hr -Sinfonieorchester (O29) and Porto Alegre Symphony Orchestra (O57).
The Bonacich algorithm determines the measure of centrality as «individual's status is a function of the statuses of those to whom he or she is connected» (Bonacich, 1987). This is the probability of the number of paths that can be directly or indirectly connected to each orchestra in the network, so the most central orchestras will be those whose contacts have the greatest number of connections on the information of the network, their contacts are the best positioned within the network (these are the best friends of the most connected actors in the network). When a positive value was assigned to beta (+β), the orchestras with the greatest influence over the best positioned orchestras were found: Minas Gerais Philharmonic Orchestra (O37), Orchestre de Paris (O50), George Enescu Philharmonic Orchestra (022), BBC National Orchestra of Wales (O4), Tula Philharmonic Symphony Orchestra (O84), Russian National Orchestra (O67) and Swedish Radio Symphony Orchestra (O78) [Figure 9]. While the lowest beta values close to zero were to: Bergen Philharmonic Orchestra (O8), London Symphony Orchestra (O32) and RAI National Symphony Orchestra (O60).
Figura 9. Centrality and Power
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When a negative value was applied to the Bonacich beta indicator (-β) to find the most powerful orchestras within the network, the findings were: St. Petersburg Philharmonic Orchestra (O74), St. Petersburg Symphony Orchestra (O75), BBC Symphony Orchestra (O7), Orquestra Sinfônica do Estado de São Paulo OSESP (O58), San Francisco Symphony (071) and Detroit Symphony Orchestra (O18) [Figure 9]. These orchestras also generate dependency on others, because, among their contacts, there are those who are poorly connected to the network.
Now, let's compare Power and Centrality. Figure 10 (a) shows a slightly negative trend between Power (-β) and Centrality (β). The St. Petersburg Philharmonic Orchestra (O74) leads the pack; it does not necessarily need the highest centrality to position itself as the most powerful orchestra, since among its contacts are orchestras that are not very popular or far away in the network.
Comparing the Degree in image (c) (without distinguishing the inputs or outputs of the node), we see that the power is positively related to the number of directly connected nodes. The same tendency can be seen in image (b), where the distribution of nodes can reflect the important orchestras, provided they are connected to other important orchestras determined by the Eigenvector.
Figure 10. Power versus measures of centrality
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In the case of image (d), power is not necessarily related to intermediation, because even if some orchestras have a zero degree of Betweenness, they still retain some power because they do not need other orchestras to reach others. However, it can be seen that the St. Petersburg Philharmonic Orchestra (074) not only has the second-highest degree of betweenness but is also the most powerful orchestra.
In image (e) an inverse relationship can be seen because the degree of closeness is the inverse of the distance, so less proximity between orchestras could be associated with power, although it is not necessarily decisive, for example, the St. Petersburg Symphony Orchestra (O75) is the closest, but the St. Petersburg Philharmonic Orchestra (O74) is the most powerful.
Interpolation
The following question may be constantly on the minds of music lovers: what programmes am I missing? The reason for which an interpolation estimate was made to find new sources of broadcast music, the answer seemed more than obvious, but it was essential to measure it. From the broadcasts of the 92 orchestras that performed the 51 works (repeated at least three times), a Kernel density raster was created by georeferencing by city.
Figure 11. Interpolation by city
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Each starting point represents the weight of the orchestras and the number of works performed [Figure 11]. An estimate was calculated based on the locations where a larger number of broadcasts is possible; the range is coloured from yellow to red, from smallest to largest. In the 3D representation, this is still somewhat exaggerated. While it is true that a greater number of programmes come from Russia, new sources of live music broadcasts are more likely to be found in Europe
Influence by country
We might ask whether the saying «birds of a feather flock together» can be applied to this network, and try to answer the question of how the orchestras are related as a group by classifying them according to their country of origin.
Figure 12. Heterophily in the network
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The result was an E-I index of 0.686, indicating a high degree of heterophily (Krackhardt et., al 1988), that is, the orchestras have a greater number of relationships with orchestras outside their group or country, and these connections run mainly through Russia, Germany, and the United Kingdom.
Discussion
The piece of reality that we are trying to understand through this analysis corresponds solely to classical music broadcasts available to everyone and covers the period from May to December 2022. It is evident that there is a large flow of free live broadcasts, and this clearly contradicts the false «thought» that it is an «art form that is perceived as an example of snobbery, sobriety and racial privilege». Anyone who has enjoyed classical music can realize that it requires discipline, hard work, and creativity, just like any other field.
As for the concern that music accessibility risks «drowning out the music itself», there is no evidence that live streaming threatens music, since most music is performed in a strict format. The most frequently performed works are by composers who can be described as "classical" For example, Beethoven was the most popular composer during the study period. There were very few programmes that mixed orchestral music with rap, electronic, pop, or other genres. These types of hodgepodges are always announced in advance and are usually offered by German sources, which few of us attend. So, the question is why the orchestras broadcast mainly works of classical composers?
Let's start with the simplest. Orchestras are also economic units that manage their own assets, human and material resources, and, like any economic unit, seek to maximise utility. At the microeconomic level, the number of works performed and the average production time are inversely related, the learning curve initially has a negative slope, and a longer learning time means less production and higher costs (Wright, 1936). In summary, it would be more economically productive to interpret "repeated" works because they have accumulated knowledge, so they perform the same works more frequently.
The following approach is a bit more sophisticated, it would be the one proposed by Robert Merton. We could refer to The Matthew effect in music because it could help us to explain the processes involved in the recognition and prestige of composers and their works. The Matthew effect, according to Merton, describes an unfair evaluation system in science in that the most famous names of scientists receive overvalued recognition for their contributions because the scientific community can easily remember their names; it is a reward system that «tends to give the credit to already famous people» (Merton, 1968 p. 57). This mechanism is doubly unfair in that it underestimates the achievements of people who are not recognized (and therefore not famous) by the scientific community, and at the same time overestimates the achievements of people who are already famous.
It is about a scientific institutional system that commands respect and fame -but for many could become a kind of fraud- this scientific circle could make big mistakes, perhaps a reason that leads Thomas S. Kuhn (1962) to write his much-cited book. Let's pause to reflect about the various reasons why Merton called this unfair reward system The Matthew Effect when he referred to The Parable of the Bags of Gold (Matthew 25:14-30) and took out the phrase «For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath». Obviously, the Bible is the most widely read book of all time, and perhaps Merton wanted the reader to quickly recognize this verse; or because the Gospel of Matthew was the most important in the transition from Judaism to Christianity, making it universal; or to make it clear that we live in a secularized world. We will not know exactly why he made this choice.
However, it is important to say that the Parable of the Talents begins by referring to the Kingdom of Heaven, and let us remember that the Kingdom of God, of our Christian God, bases our existence on Love, Love for God, Love for others. In the Gospel of Matthew, a similarity is made with the intention of evangelizing, but unfortunately it is misinterpreted, because the characteristic of the "modern" human is precisely the denial of the existence of God. It is obvious, and Merton proved it, that there is an unjust reward system in science, but he, faithful to the "modern" tradition, did not think of another title. His opinions are understandable and could perhaps be applied to the reward system that composers receive in the world of music.
During the 1990s, the use of the World Wide Web initiated a massive and explicit revolution in communication systems, commerce and economics, as societies were plunged into a new, faster and lower-cost global “era”. The Internet became a concept that condenses «experiences» of the type that relates multivocal semantic networks that have transcended and is projected over time, a concept that sinks into the field of socialization and can be an indicator of historical structures. This concept could define what Reinhart Koselleck called «time strata» which refers to the accumulated experiences of individuals and generations, in such a way, a twenty-something cannot understand social relations without using the Internet, they could feel deprived, their generation perceives the world differently than their parents and grandparents.
We do analyze the «experience» both in its lived reference and in its methodological research reference. Surprising and repetitive, cumulative and changing experiences that generate histories and have the capacity to transform an entire social system. However, the process of accumulating new experiences leads to the loss of others experiences. Let's take the case of Beethoven's surprising works, they produced cumulative experiences and made it possible to structure long-term histories, they became other people's experiences, which were incorporated into one's own experiences, which passed through the generations and became a kind of historical experience «supra-generational» and stabilized in the long term. Any changes they brought must have been gradual and almost imperceptible over the centuries. From Koselleck's perspective of history, then, a change in experience would have to include factors of «surprise», «repetition», «accumulation» and «displacement» of other experiences to bring about such a radical change in music until it drowned itself. But let's think, how could the internet and mass accessibility backfire on music itself?
The easiest way to explain this is with the theory of value we all know: The greater the supply, the lower the price, like the paradox of the value of water and diamonds, for which marginalist theorists later found a solution. The truth is that there are already investment funds for the global water market. What would Smith have said about that? That is the vice of putting everything on the market.
The main concern of making music more accessible or «democratic» relates to the risks of disruption (let's just call it marketing) to attract the attention of a younger audience. A recurring solution seems to be the mixing of musical genres, but as we have seen, it would take experiences as profound as Beethoven's to create real disruption (but anything can change), and these would require other elements. The truth is that Internet broadcasts of programmes are currently subject to strict rules that we all know and appreciate very much, as most of us have expressed in the YouTube chat club.
Reflection
If we were to ask the most powerful orchestras on this network (live broadcasts), that is, if we were to ask today to St. Petersburg Philharmonic Orchestra (O74), St. Petersburg Symphony Orchestra (O75), BBC Symphony Orchestra (O7), Orquesta Sinfônica do State of São Paulo OSESP (O58) and Detroit Symphony Orchestra (O18) for their opinion on the “thought” that the music they make is «perceived to exemplify snobbery, congestion and racial privilege», what answers would we get?
There is a big difference between thinking and knowing. For example, we think about the existence of God but we do not know if he really exists, and that statement is acceptable to everyone. We know that this kind of music is art, but we think that it is reserved for a certain part of the population and this statement is also acceptable to everyone. So it would be a social system that sometimes accepts and expresses some thoughts without fear or shame, without knowing them; while on other occasions it accepts certain knowledge as thoughts with fervour and joy, without questioning. Then, it would not be a world that distinguishes between knowledge and thoughts, but one that is tailor-made.
We might ask ourselves, what kind of tailoring would adapt to a world that arbitrarily selects thoughts or knowledge? The immediate and most obvious answer would be, well, our social system is indeed closely linked to economic relations, but the human tendency to group and distinguish between one or the other is perhaps anthropological. Then, music does not divide, segment or label the population, but it is easier to look only at the tip of the iceberg.
The remembrance of visiting a theater to enjoy a live performance could include numerous pleasurable experiences such as appreciating the architecture, feeling the texture of the books and smelling the ink on the pages, looking closely at the sheet music or having a coffee during intermission. Thus, one could argue that virtually all the senses are engaged when attending such an event, which would result in the formation of memories. However, the online audience, lacks some senses, including the sense of sight. Especially in radio broadcasts, our memory might include expressions such as “what a precise movement”, “this piece was taken at a perfect speed”, “I couldn't stop listening to it again”, “I felt emotional all day”, “the encore was dedicated to”, “the curator said”. The absence of some senses causes the brain to create other types of memories that are perceived only with the senses used. The partially blind online audience cannot perceive skin color or ethnicity, and therefore could not find clues as to what "snobbery" and "stuffiness" mean from those who make music or who attended.
This mechanism works both ways. Orchestras can know the age range of the listeners, the countries -or cities- where their music is appreciated, but they lack sight, they can not see either. They cannot know if the art they make could be determined as «racial privilege». The orchestras do not know the tastes, preferences or financial situation of the audience because they receive zero dollars from it. Both, the digital audience and the orchestras could "think" or suppose but not "know" the events that occur in a given period. Therefore, this "thought" about classical music is not valid, at least not for online broadcasts.
The concern about how to make music accessible without lowering rigorous standards and artistic quality could become a riddle. Short programs have been made, even with slightly weird mixes to reach a wider audience. However, art and music cannot escape the network of social relationships. In a world where one moment a pandemic, earthquake, or war can shake the whole world, the next, information floods our screens with images of celebrities walking the red carpet. It could only allude to an ephemeral world, a plasticized one, in which human could have become what Gilles Lipovestky describes as Zombies. How can one escape from this system of social relations?
But let us remain calm, because the conceptual history would tell us that the real transformations would also be reflected in language and in the emergence of new words and concepts, such as the Internet. For the period we have been studying the online broadcasts, we have verified and now know that the programmes generally consist of works by composers we know as "classics" in a strict format, even with an intermission.
On the future
It will be very interesting to know if the same trend between works and orchestras will continue in 2023. With a greater number of programmes and diversification of broadcast sources, the contagion analysis could be carried out to see how the behaviour of one orchestra might be influenced by others. Remember that all suggestions are welcome for discussion and we have enough time (Deo volente) to think about creating the next analysis.
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P.S. My apologies for the grammatical errors found in this post, I will improve my writing skills in the future. Sending lots of love to everyone!!💗💗💗
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