Melting Pot: An ambiguous series combining minority and majority discourses
Bibliographic record
Abstract
Melting Pot est une série francophone produite par la RTBF, l’une des chaines télévisées publiques francophones. La RTBF est un média officiel et national, et comme tel, il ne fait pas partie des médias minoritaires. Melting pot reflète cette situation puisque les protagonistes sont des membres de la majorité ethnique et culturelle de la population (francophone, blanc et belge). Cependant, face aux Flamands néerlandophones, les Belges francophones constituent une minorité en Belgique, un pays caractérisé par un conflit linguistique et politique, et Melting pot est aussi représentative de cette situation ambigüe à travers les intrigues secondaires et en élaborant un réseau complexe de significations autour du symbole représenté par le café le Melting Pot (à la fois un lieu, un biotope de personnages et un jeu sur la notion de “melting pot”). Cet article est une étude de cas basée sur l’analyse du contenu des 3 saisons de la série. Abstract: The RTBF (public Belgian television) can hardly be considered as a minority media. However, in the context of fictional production, the RTBF is not a powerful actor. Melting Pot is the only large-scale series currently produced by the channel. We can thus qualify it as a triple media exception: Belgian, French speaking, series. The fiction takes place in the Marolles district in Brussels. This area represents the “Belgian melting pot”: a mix of people, languages, origins... But how are these communities and languages represented, notably the French speaking (a minority in Belgium and a majority in Brussels) and the Flemish (a majority in Belgium and a minority in Brussels)? The question makes sense in a country divided by a political crisis for more than one year and where the question of identity crystallizes the debates. This article will put in context the Belgian production of fictions. A content analysis of the representations conveyed by the series will constitute the main part of the communication. An interview with the producer will unveil their initial intentions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".