Avril 2012. Diagramme des relations entre notions, auteurs et corpus utilisés dans ma thèse. Cliquez pour agrandir
Voici une petite tentative – en perpétuelle évolution – de préciser quels sont, ou seraient, les ponts reliant des concepts-clés issus des disciplines structurant ma recherche sur les séries TV turques. Ces disciplines sont : les sciences de la communication, la géographie, et le binôme sociologie/philosophie. J’ai ordonné ces concepts selon la logique suivante : un concepts original (cad dont il est possible de préciser quel en est l’auteur) peut être relié à des disciplines particulières ainsi qu’à des “inter-concepts” ayant plusieurs acceptions selon le champ disciplinaire depuis lequel ils sont énoncés. Ce point me semble important car le fait de ne pas préciser les sources ces “inter-concepts” peut conduire à une multitude d’interprétations, et les transformer ainsi en mot-valises vidés de leur sens.
Archive décembre 2011. Cliquez pour agrandir
Afin que cet exercice ne se cantonne pas à la théorie il est aussi nécessaire de relier ces concepts et ces disciplines à un corpus précis et circonscrit. Le second objectif de cet exercice est donc de retracer l’effectivité de mon corpus, en reliant ses composantes aux concepts centraux et périphériques de ma réflexion. En effet chacune des sources de données ou d’information se doit de répondre à une exigence à la fois scientifique et méthodologique afin d’être analysée et de participer ensuite à l’échafaudage de la thèse.
Enfin, il est important de préciser que les cartes mentales présentées ici ont une vocation avant tout épistémologique, et qu’elles sont donc amenées à se transformer durant le travail de thèse. C’est la raison pour laquelle l’archive de décembre 2011 est elle aussi visible dans ce billet, pour témoigner d’un processus. La carte mentale des concepts est comme une sorte de photographie de ma problématique à un instant précis, photo dont la fonction serait à la fois de garder une trace des champs abordés au préalable, tout en rappelant constamment à l’esprit qu’il faut faire évoluer cette réflexion vers plus de précision. Cette évolution constante trouve sa source dans les nouvelles lectures, les dernières références ou dans l’avancée de l’enquête de terrain.
Petite généalogie d’une nouvelle pratique :
D’un point de vue uniquement pratique, l’envie de cartographier ces concepts est venue de la manière suivante. Au dépôt du sujet, j’avais comme tout doctorant une bibliographie de base. Une fois la thèse commencée depuis plusieurs mois, lorsque j’ai voulu reprendre cette bibliographie préparatoire, l’exercice m’a alors semblé vain avec les outils que j’avais à disposition : à quoi bon peaufiner une bibliographie sachant qu’à chaque article lu les références évoluent ? Comment s’y retrouver dans un fichier word de dizaines de pages, même thématique, lorsqu’on veut s’atteler à une recherche transdisciplinaire dont le but est de jeter des ponts entre les disciplines plus que de chercher l’érudition dans une disciplines particulière ? Et tout simplement quel outil utiliser au quotidien, outil qui puisse accompagner les lectures de la façon la moins invasive possible ?
Trois grands champs nourrissaient de manière abstraite ma démarche (géographie, communication, sociologie-philosophie). Certains de ces concepts m’étaient tout à fait familiers car ils m’accompagnaient depuis plusieurs années déjà. D’autres étaient pour moi nouveaux car je les avaient découverts dans les premiers mois de la thèse, lors de la préparation plus fine du sujet, ou encore dans des lectures très récentes. Les premiers mois de recherche m’ont aussi forcé à définir un corpus plus précis et réaliste (qu’est-ce qui est à disposition sur place?), chose qui force en retour à trouver les concepts ad hoc permettant d’interpréter ce corpus. Complexité supplémentaire tous ces concepts, tous ces auteurs, toutes ces informations, possédaient des échelles et des niveaux d’abstraction très divers : certains concepts sont très abstraits et très englobant ( qu’est -ce que le “libéralisme” ou la “post-modernité” ?), quand d’autres lectures sont extrêmement situées dans le temps ou l’espace et sont purement informatives (tel immeuble a été construit par monsieur X telle année).
Peut-être par goût de la symbolisation et du graphisme, ou du fait de ma formation en architecture (dessiner des plans sur des grands formats) ou encore bêtement par jeu, j’ai d’abord tenté de tracer à grand traits la carte de ces familles d’idées qui à mon sens avaient un lien entre elles. L’intérêt de cette deuxième dimension, celle de la carte, est comme on peut le faire sur une table de pouvoir “mettre à plat”. On peut alors faire des regroupements, trouver des gradiens (du plus au moins abstrait), des filiations, voire de créer des parallèles entre des idées qui ne sont pas évidents à priori et de les tester. L’exercice est ensuite devenu de plus en plus prenant au fur et à mesure que la pratique de ce logiciel m’est devenu plus familière et que le terrain avançait, apportant avec lui nombre de nouvelles questions d’ordre sociologique, historique, méthodologique.
A défaut d’encourager d’autres à faire de même, j’essaie d’engager les autres chercheurs ou étudiants avec lesquels je discute à “rentrer” dans ces cartes, à – comme face à un plan de bâtiment – tracer des itinéraires, des parcours imaginaires, ou à errer, flâner, sans but dans des cartes de ce genre. Flâner d’une idée à une autre en quelque sorte. Si on met de côté l’aspect technico-technique (le fait que ce soit un logiciel par exemple), ce type d’outil n’est jamais qu’un autre outil de représentation, ne remplaçant pas l’écriture et le récit mais offrant la possibilité du changement d’échelle (le zoom), la sélectivité (les calques), la symbolisation (les formes basiques et les couleurs) et une deuxième dimension de lecture par rapport à l’écriture (la co-existence d’éléments).
Pour information, les diagrammes ont été réalisés avec le logiciel libre VUE dont j’ai déjà parlé dans un précédent article.
Si vous souhaitez voir cette présentation dans une résolution correcte merci de poster un commentaire juste en dessous.
Article créé le 300/11/2011. Mis à jour le 05/04/2012
As I’d like to make a representation of the media groups in Turkey I looked for some works previously done on this kind of topic. Those two graphs found on the web illustrate the links between audiovisual and press production in one hand, and the media groups in another hand. Those representations are very helpful to give an idea of the complexity of media sector and of the interelations between finance, media groups, producers, technicians, and so on…
Financial links between cinéma et media groups in 2010 (source CNC). Click to download the complete pdfMedia groups in France in 2004. (source : internet). Click to dowload the complete pdf file
See also the Pierre Carles documentaries on media groups in France, particularly on TF1 channel : “Fin de concession” (2010), “Pas vu, pas pris” (1998), “Enfin pris?” (2003)
Bonus :
Photogram of the Pierre Carles documentary, “Fin de concession” (2010). Extract of a printed interview of Patrick Le Lay, TF1 CEO.
My translation to english of the quote in pink (from “Les dirigeants face au changement. Baromètre 2004”):
“[…] let’s be realistic : the basis of TF1 job, is to help Coca-Cola, for instance, to sell its product. But to make the advertisement message perceived, the brain of the spectator has to be available. The vocation of our broadcasts is to make it available, so to say entertain it, to relax it to prepare it between two messages. What we sell to Coca-Cola is available brain time. […]”
En revenant du colloque CIST , ayant vu tant de personnes parler de “territoire” depuis autant de disciplines scientifiques différentes, plusieurs questions et commentaires se bousculaient : quels points communs avaient toutes ces définitions du territoire ? Où y avait-il des ponts entre les disciplines ? Bref, comment puis-je m’approprier ces interventions ?
.
J’ai retenu plusieurs hypothèses théoriques (restant à vérifier) qui me semblent récurrentes dès qu’il est question de territoire. Voici donc une petite réflexion théorique – sous la forme d’un jeu assez basique de mathématiques combinatoires – dont les hypothèses sont les suivantes :
Un territoire est lié à un réseau, lui-même relié à un “milieu associé” (B. Stiegler).
Un territoire peut être :
ouvert, fermé ou poreux. (Géographie humaine)
Un territoire peut se combiner de 5 manières avec un autre au sein d’un réseau :
Un réseau peut se situer de 3 manières par rapport à un milieu associé :
dehors, dedans ou à cheval. (extrapolation des thèses de B. Stiegler)
Un réseau peut se combiner de 3 manières avec un autre :
exclusion, partage avec un ou plusieurs territoires communs, partage sans territoire commun. (Sciences de la communication et Géographie)
La question que je me posais au départ était somme toute assez simple : si on accepte ce jeu d’hypothèses, peut-on compter le nombre de combinaisons possibles entre Territoire, Réseau et Milieu associé ?
Conclusion :
La dimension un peu abstraite de l’exercice et le risque d’avoir commis quelques approximations logiques mis à part, on voit vite que le nombre de combinaisons possibles explose dès que le nombre des territoires et des réseaux étudiés augmente ne serait-ce qu’un peu.
Ma conclusion -temporaire – serait donc qu’en géographie définir un par un les territoires que l’on étudie n’est pas suffisant. Dans l’hypothèse où un territoire est composite, qu’il s’insère dans un milieu et un réseau, qu’il peut être ouvert, fermé ou poreux, etc… le nombre de combinaisons ou d’arrangements entre les différentes parties peut rapidement devenir trop grand pour permettre de tirer des conclusions scientifiques solides.
A la définition précise des caractéristiques de chaque territoire il semble alors essentiel d’ajouter une analyse terme à terme des liens entre chacun des éléments étudiés (dont le nombre Ima, slide 3, reste plus limité), c’est-à-dire effectuer une analyse relationnelle, complémentaire à l’analyse de terrain, afin d’approcher au plus près la réalité du fait étudié.
Click to enlarge. Simplified hierarchy of relationship between actors of audiovisual production. Made with VUE
In complement of my previous article I wanted to present some maps made with Gephi, an amazing program (opensource in Java) allowing to explore and spatialize relational databases. The biggest problem is indeed to create your own relational database, what I tried to do using the datas of audience I grabbed on Internet (see also here). The main idea would be to follow this scheme of analysis to show and measure the process of creation of audiovisual content as TV series.
The scheme is a pure abstraction, and once the database put into the program of spatialization it begins to complicate a lot : the dataset is composed of approximately 500.000 entries, each one having the name of the program, the typology, audience score, rate, channel of broadcasting, and so on…The maps are made from datas going from april 2004 to august 2011, and are separated by group of study : in one hand the “AB” group mostly aimed by advertisers, and in another hand the “TÜM” group gathering the whole sample of study.
So the first maps shows the relations between channels and the typologies of programs channels broadcast. We won’t be surprised to find the same patterns of results I showed in this article, but I think those figures are more readable.
AB GROUP :
AB group - first 10 audiences - from april 2004 to august 2011. Sources : Medyatava / AGB Nielsen. Made with GephiAB group - first 100 audiences - from april 2004 to august 2011. Sources : Medyatava / AGB Nielsen. Made with Gephi
Those maps shows the preference of the audience for turkish series (link more visible in the 10 first rates than in the 100 first).
TUM GROUP :
(In construction)
WHAT’S NEXT ?
The next game I’ll play with those datas will be to cut them into laps of time (by season or by month) and observe the film of the evolution pictured by AGB Nielsen of the audience from 2004 to 2011.
More infos on Gephi and other programs to do datamapping here :
By a kind of laziness I was searching a program allowing me to save some time on the georeferencing process. So there are some maps I did with Batchgeo from a list of TV and film producers (app. 290 enterprises); a list of TV channels (app. 430 channels); and a list of the malls of Istanbul (89 sites). The software is supposed to geolocalize automatically the enterprises from their addresses using Googlemaps and its algorithms… I’d confess I’m not very sure of the precision of the results but it spare a lot of time at the first glimpse, at least on the technical part of georeferencing. I’m trying to improve the quality of the datas to get a better picture…
An interesting thing is you can also transform those datas from a Google Earth format (importing the .kml files) to GIS formats (.shp and the rest) through some tricks (visibles here and there), or using GvSIG to convert the kml files…
Ce billet de synthèse a pour but de préparer les entretiens de terrain 2011-2012, permettant de compléter mon étude par une approche plus qualitative des modes de production et de création des séries télévisées en Turquie. Le choix de ces titres s’est opéré en prenant compte des critères suivants : diffusion sur des chaînes privées différentes, taux d’audience importants, séries en court de tournage sur la période 2011-2012, genres différents, accès aux plateaux de tournage ou/et avec des personnes impliquées dans sa réalisation (acteurs, producteurs, scénaristes, diffuseurs).
I used excel’s macros programming (cf. this article for the technique) to gather into one database several informations about the daily audience of television in Turkey since 2004 containing : target group, day of diffusion, name of the program, channel of broadcasting, score of audience, rating, and market share. The period studied in that database runs from June 11th 2004 to May 1st 2011, and represents approximately, so 2500 days and 500.000 different programs referenced.
NA : in order to share those results with turkish colleagues more easily I tried to write this article in English. I apologize in advance for the grammar faults I’ll certainly do. You can post comments if some parts seems not understandable.
I considered that part of my thesis as a complement to other datas I presented into another articles (timelines, TV production informations). On the frame of a scientific research the global intention of this new quantitive work is to prepare, by gathering various quantitive facts or infos about my subject (tv turkish series production from 1970 to now) some sufficient factual backgrounds to lead further qualitative and theorical analysis.
THE DATAS :
Before any consideration I wanted to give some infos giving an idea of the turkish population concerned by the television. In Turkey, for a population of 72 millions inhabitants and 18,3 millions households in 2009, 17,9 millions have a tv (so 96%). It means around 71 millions turks were in 2009 able to watch television at home. (source : EU, MAVISE )
The audiences datas presented below were first produced by AGB Nielsen, then published day by day on Medyatava since June 2004.
According to their website “Nielsen Audience Measurement Turkey has been conducting Television Audience Measurement (TAM) services in Turkey since 1989. Today, the panel represents the urban Turkish population with 2500 households“.
KEY FACTS Population: 72,561,312 total / 60,264,546 urban (2009 population) Panel Size: 2,500 households First data production: 1989
THE PANEL Peoplemeters installed: 3,500 to 4,000 Universe: 51,657,783 (representing age 5+ urban population) Data monitored: Terrestrial, cable and digital satellite Data retrieval via: Phone lines
(Source : AGB Nielsen)
The main critic we could do about the audience survey by AGB Nielsen is the lack of precisions concerning the method they use to obtain their numbers. It’s also interesting to notice that AGB Nielsen is nowadays living the end of a crisis lasting since 2007 between them and the turkish government (cf “revue de presse” and this article), their contract coming also to an end. Anyway the interest of doing this study is the monopolistic position of AGB Nielsen in the audience measurement in Turkey. Even if the figures given by AGB are suspicious, the fact stays that every private channel has to refer to their numbers to decide of a broadcasting strategy.
Another precisions and definitions concerning the reading of the graphs :
Audience rating : from 0% to 100%, 0% meaning nobody watched the program, 100% the whole sample population watched it.
Audience score : from 1st to 100th place. A good score could be a high or low rate depending on the other audience ratings ; it’s relative.
Number of programs : quantity of programs broadcasted.
AB group / TÜM group : groups of population defined by AGB Nielsen. I don’t have yet a lot of information about the groups composition. So as a first exploration, this analysis will just focus on one : the AB group, seeming to be the “high and middle class”, the one interesting the most for advertisement sellers.
AUDIENCE ANALYSIS :
I’ll present therefore a serie of various graphics intending to measure the importance of TV series audience amongst other kind of programs into the grid of the channels. At this step of the analysis the audience analysis can be approached by different ways : the program typologies, the channels grid’s structuration, and the periodicities of audience.
The audience data is a key fact for any channel studied : the audience weights the price of the advirtisement space in the broadcasting grid, and so the capacity of revenue of the channel. The higher audience gets, the more expensive you can sell advertisement. It seems quite important to analyze audiences and its impact on creation considering the behavior of other channels as in Turkey as abroad : for instance it’s well-known in America the channels are monitoring very carefully the audience’s curves and make changes to the contents, to the scenario, to get better audience rates. The situation in Turkey seems to be likewise and any telespectator in Turkey could see what importance and presence got the sponsors into the production of a tv serie.
1/ PROGRAMS TYPOLOGIES
The first graph presented above compares the audience score rates (from the 1st viewed to the 100th) of different categories of programs :
Cartoons, Concerts, Daily Magazines, Documentary, Film Foreign, Film Turkish, Health/Service Education, Humour, Magazines, Meeting/Discussion, Music/Entertainment, News report, Newscast, Politics, Reality Show, Religious, Serials Foreign, Serials Turkish, Sport News, Sporting Events, Sporting Magazine, Studio Programs, Talk Show, Theatre/Cabaret/Musicals, TV Games, Weekly Magazines, Women.
Audience scores analysis by programs typologies (click for a full view)
Reading the graph : We can see on this graph a very distinguished preferences from the audience (10 first rates ) for turkish series and newscasts, the third prefered kind of program being the TV games. The particular observation with this chart is the relative constance of those curves for every typology from 10th to 100th rating score. The interpretation we could do at this point (as a hypothesis) would be the following :
Above another graph in the same spirit but slightly different. In that graph the scores (x axis) are replaced by constant ratings, so the interpretation deals with another fact : the “left-behinds” turkish series programs.
Audience rating analysis by programs typologies (click for a full view)
Reading the graph : In my interpretation of this graph, the “left-behinds” turkish series are the programs in orange at the left side of the graph, composing the big orange curve. Those programs get a very low audience (less than 10% on the x axis) but are massively produced ! That fact seems quite paradoxical but could be seen as an effect of the inflation of the tv series production – the so-called “Yesilcam” era for tv series. It could be explained by the will of producing more and more series, hoping one of them gets good rates. Series being the most successful way to have audience, more producers invent series concepts. Logically this “liberal” way to produce generates more offer than demand, and we can imagine half of series production has just succeeded in starting but not lasting. This could be the dark side of series production : the inflation of ideas and careers left behind because of their lack of capacity to get audiences.
2/ CHANNELS
The previous graphs were more dealing about the repartition of the whole programming between various typologies, without precising on which channels they were broadcasted.
The next graph shows how every channel compose its own grid of programs.
Channel programmation composition by typologies (%) (click for a full view)
The next graph shows the numbers of TV programs rated from 1st to 10th, classified by channels.
Scoring audiences by channel (click for a full view)
There is another way, perhaps more explicit, to show those figures using the percentages.
Audience scoring audiences by channels (%) (click for a full view)
Reading the graph : On the 18 channels presents in the AGB Nielsen datas, only 4 are getting more than 80% of the 10th first rates. So, if we trust AGB sources, the spectators are mostly watching ATV (in blue), KanalD (orange), Show TV (clear blue) and Star TV (pink). It’s also good to notice in the margin that TRT is treated differently by AGB Nielsen since 200…
The interesting fact showed by these numbers is the constance of this repartition in the 10th first rates.
3/ PERIODICITIES
Another important factors of the composition of broadcasting are the cycles of programs typologies choices within a day, within a week, or within a year. The time factor is a complex data to compute on a day-by-day database.
The two next graphics shows clear cycles on year-scale illustrations :
Audience ratings for 1st score by major typologies (click to a full view)Average audience rating by day of broadcasting for the 1st rating score (click to a full view)
Reading the graphs : every summer (between june and august) since 2005 registers a clear decrease of audience. We could also note that turkish series (in blue) gets most of the highest rates the whole year compared to other programs, as we can see by the “clouds” of high rates.
The serie of the next three graphics focuses on turkish series programmation and their periodicity by month :
Turkish series analysis by month - average / quantities (click to get a full view)
Turkish series analysis by month – average / percentages (click to get a full view)
Turkish series analysis by month, year of programmation and audience scores - quantities (click to get a full view)
Reading the graphs : As we’ve seen before, the summer period could be considered as an empty one for series. But here the curves for all audiences scores and 10th first audiences scores are very different : as the total of programs broadcasted is merely stable the whole year (just february, march and june registering a light decrease), the curve of best scores is much more flexible and increase from january to march. We could input that to a big difference of strategy of diffusion between series in general and series gaining the first 10th scores. Perhaps after a couple of month of tests, the less competing series are ended at the benefit of the other, then more broadcasted
Furthermore the last graph shows the increase of serie’s diffusion from 2005 to 2010 in the absolute, with two years having real particularities : 2008 (the month of August for first audience score shows a peak) and 2010 (the absolute quantities of series broadcasted for all scores and for the first score).
The next graph focuses on a week-scale analysis, showing the percentages of programs broadcasted by their typology every day.
Typologies by weekdays (%) (click for a full view)
Reading the graph : Once more we can see the importance of turkish series in the grid compared to other kinds. Series are mostly working-days programs, the saturday and sunday gaining good audiences with other kinds of programs like TV games. For instance the mondays, the first 10th best audiences are usually generated by turkish series (50%), then by newscasts (25%), and the rest stays under 10% or less.
LIMITS AND CONCLUSIONS :
To be understood clearly, I wanted to precise I tried to keep in mind during this study that the figures given by AGB Nielsen should be taken with many precautions : first because of the different complaints opposing AGB and the government on the accusation of manipulating the datas, and secondly because of the absence of another major channels like TRT. So it’s clear AGB figures doesn’t have to be taken as a reflect of the reality of the audience, but as a tool inside the TV production chain.
Taking in consideration those limits the interest of this database lies anyway in the possibility to show how the different program typologies, the channels, the schedule of broadcasting and the ratings are interrelated, constituting a field in a Bourdieusian way to speak. To complete those diagrams by a field analysis, a factorial correspondance analysis would be necessary and should be the main topic of a next article.
As a first conclusion it’s obvious to say turkish series have a strategical importance in the channel’s capacity to get audience, and mechanically to get revenues from advertisement. More than strategical it’s nowadays the first kind of program getting best audiences. This capacity reveals interesting characteristics of the TV production for private medias in Turkey : even if in Turkey the topics of series are very dependent with local cultures and so differentiate production from US, the logic of “market” and the inflation effects related are similar.
A second conclusion to this article would be the help those analysis gave to give more precision to my phd thesis. It’s one thing to know that generally tv series are important in the television panorama, it’s another to be able to give precise datas about it. Furthermore, crossing those reflexions with previous ones, those analysis allow me to choose with more arguments a reduced panel of channels (ATV, SHOW, KANAL D and STAR TV) being the channels whiches are in the same time major producers of TV shows, and the most dependent on audiences rates in their capacity to attract advertisement funding.
Last update : may 5rd, 2011 (262 historical events in 10 categories)
I just produced a basis for a timeline mixing some historical datas I wanted to compare : economics, medias, political events, global context, demography, etc. related with Turkey’s and Istanbul’s history. The purpose of this little work is to contextualize on a century scale the apparition of TV medias in Turkey and in Istanbul metropolis, and also to share with other searchers a timeline resuming Turkey’s important events.
This chart would need more further precisions, especially on sources, and would need more informations on medias major events (legislation and so on). So this article should be updated regularly…
NA : This article and the chart are in english because the sources were so.
Click on the picture to a full-view size
The events are classified between 10 families of events :
01 – INTERNATIONAL CONTEXT
02 – ECONOMY
03 – POLITICS
04 – MEDIAS
05 – ISTANBUL GOVERNMENT
06 – INDUSTRIES
07 – CULTURE
08 – TRANSPORTS
09 – URBANIZATION
10 – BUSINESS DISTRICT
Sources : “Development chart of Istanbul metropolitan area” / N. Monceau (dir.) “Istanbul”/ J.F. Pérouse “La Turquie en marche” / Wikipedia
Travail en cours / dernière mise à jour le 18/03/2013
