Principal Ongoing Mutations of Cultural and Informational Industries
Bibliographic record
Abstract
For over three decades, a group of scholars has developed the Cultural Industries School. 1 Some of the key fi gures, main contours, and diffusion of this school, especially in Europe, Latin America, and parts of Canada, are outlined well enough in the Introduction and in Chapter 2, so they do not need to be repeated here. Instead, this chapter provides an update on a collective research program organized by practitioners of this approach in 2004 at a seminar at MSH Paris Nord. Its participants call this research program "Mutations des CICI" or, translated into English, "Mutations of Cultural, Informational, and Communications Industries." This research project is the next step in a trajectory of research that others and I have carried out for many years under the framework of the cultural industries approach. The goal is to grasp the contemporary mutations affecting the cultural, information, and communications industries. Sometime in the future, we also intend to examine whether it makes sense to distinguish between the now fashionable notion of the creative industries and our focus on the cultural industries. For the time being, however, it can safely be said that we are skeptical that much would be gained by a change in nomenclature at this time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| 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".