Defining Culture and Communication for the Purpose
Bibliographic record
Abstract
Claude Martin is an economist, professor at the Département de communication of Université de Montréal. He teaches courses on cultural industries, media history and culture statistics. His research is focussed on culture and communications statistics, development of cultural industries in Québec, best selling books in Québec and television programming. He is conducting research for the Observatoire de la culture et des communications du Québec and member of Statistics Canada National Advisory Committee on Culture Statistics. Culture statistics need needs a stronger definition of its border and structure. It is a relatively new field that started as industrial data for the media. In search if a strict definition, it is proposed to include in Culture and Communications only the production of symbolic goods and services. Cultural labor can extend outside of the domain of Culture and Communication. The domain of Culture and Communications can be structured as a communication (sender- message- receiver) and the supply side can use the triad of creation- production- distribution as structure. A domain of regulation must be added to the model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".