Facebook as « the media » : analyse de la convergence médiatique autour de facebook
Bibliographic record
Abstract
Resume : Depuis son entree en bourse, l ’ entreprise Facebook est soumise a des pressions considerables de rentabilite. Elle doit assurer une croissance continue, qui ne peut a long terme etre basee seulement sur la croissance de la base d ’ utilisateurs et d ’ utilisatrices. Cet article cherche a analyser les strategies mises en place par Facebook pour se diversifier et ainsi assurer cette croissance. Principalement, nous cherchons a montrer comment Facebook s ’ impose comme entreprise mediatique globale et opere une convergence mediatique a grande echelle autour de sa plateforme. Nous analyserons pour ce faire de recentes initiatives de l ’ entreprise en revisitant les definitions des termes « entreprise mediatique » et « convergence » a l ’e re num erique. Abstract: Facebook’s IPO in 2012 put the company under intensified pressure for profitability and growth. The company’s growth, though, cannot be assured only by the increasing number of users. This article aims to analyse the different strategies operated by Facebook to diversify itself and therefore ensure its growth. We mainly aim to demonstrate how Facebook is imposing itself as a global media company and how it articulates a large-scale convergence around its platform. To do so, we will present an analysis of recent initiatives of the Silicon Valley giant while revisiting the meanings concepts like “media company” and “convergence” take in the digital era.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".