Bibliographic record
Abstract
The cultural industries participate in building collective memory because, in many cases, public decision-makers have chosen to elevate individual memories to the rank of collective memory. Cultural industries are faced with systemic discrimination (Eikhof and Warhurst, 2013), which suggests the collective memory of these industries face the same challenges. In this theoretical article, we propose a framework based on Boltanski andThévenot’s (1991, 2006) theory of justification in order to make collective memory in cultural industries more inclusive. First, we conceptualize collective memory as a compromise between the domestic and civic worlds of Boltanski and Thévenot (1991, 2006). Then, the artists and their individual memories are presented using the world of inspiration. Finally, we propose using the world of projects to make the collective memory of cultural industries more inclusive. We, therefore, propose greater openness and democratization of collective memory in the cultural industries due to the world of projects.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".