Articulations of national identity in the bylaws of film subsidy institutions in Switzerland, Belgium and Canada
Bibliographic record
Abstract
Defining the "national" in cinema is a difficult task. It gets even more complicated if one takes into account multilingual and thus multicultural nations such as Switzerland, Belgium and Canada. In this paper I explore the peculiarities of the concept of national cinema in the francophone regions of these three countries. I approach the issue by way of taking a closer look at the bylaws of film subsidy institutions and asking how they articulate questions of national and cultural identity. My hypothesis is that the declared aim to foster national identity in cinema is jeopardized by the proximity of French cinema, especially in Belgium and Switzerland whereas Quebec regional cinema comes closest to an autonomous national cinema, although being a part of Canadian national cinema. The bylaws of the film subsidy institutions focus on the definition of a film's nationality, which turns out to be closely linked to the individual nationalities and linguistic competences of the creative personnel of the production team. The bylaws do not define any criteria for the actual content of the screenplays, the nation-state thus doesn't regulate what might be a 'national story'. This points to the fact that it is the collectivity of individuals that form a 'national production team' and thus represent the nation and its identity.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".