Technology and demographics: Are cultural habits mutating?
Bibliographic record
Abstract
Surveys on cultural practices and habits (conducted every five years since 1979 by the Québec Ministère de la Culture, des Communications et de la Condition Féminine [MCCCF]), government statistics on the production and dissemination of cultural products, and research in this field all foresee significant changes that may question the pertinence of government intervention in cultural affairs in Quebec. The aging of Quebec's population, its increasingly multi‐ethnic makeup, and the emergence of digital culture without borders favour a market model of culture management that is incompatible with cultural policy geared towards access to and assistance for creative activities. The emphasis has therefore been shifting from cultural values to market values. Utilizing data from the ministère de la Culture et des Communications du Québec surveys, and especially data from a complementary survey, conducted at Université Laval in March 2007 by a cultural development research group (Fonds québécois de la recherche sur la société et la culture [FQRSC]/concerted action initiative), the authors attempt, in particular, to evaluate the influence of technology and demographics on the production and dissemination of Quebec's cultural products, and to sketch the outlines of an “emerging model” of cultural habits.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".