Race, Representation, Misrepresentation, Caricatured Consumption Tropes; and Serious Matters of Inequity and Precarity
Bibliographic record
Abstract
We do not know if it is a trend or a temporally short uptick. At MGDR, we are noticing that there is some increase in significantly influential entertainment products – films, television programs, video series on streaming platforms, etc. – that strive to represent people, cultures and regions that have been marginal or underrepresented. Of course, from a ‘markets’ perspective, it makes sense – in rapidly diversifying societies such as United States, Canada, United Kingdom, France, Germany and Australia – to create entertainment products that supplement the large corpus of ‘mainstream’ entertainment products. In other words, the mainstream needs to start accommodating – and, we hope, merging and blending with – parallel sub-streams. Indeed, in all aspects of life, especially in the United States – and, from there, echoing worldwide – there is increasing evidence of multiracial and multiethnic representation in foodways, fashion, films and more. Given all this, we at MGDR have decided to strive to feature and analyze, in multiple issues of the journal, the emerging patterns of new or novel representations. In this issue, we focus on some films. First, the focus in on a film that, while partly cinematic fiction, also has very substantial elements of documentary-style realism. The second set of films – the original and its sequel, after a 30-year gap – deal with the relationship with the African-American culture of New York, and a mythical well-off nation in Africa.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".