Algorithmic biases and the discoverability of digital cultural content
Bibliographic record
Abstract
The digital era has transformed how the production of culture is accessed, how it circulates, and how it is organized. In this article, I wish to discuss the notion of discoverability. This notion, I argue, is one of the most recent cultural policy instruments that has emerged in the digital era. Discoverability implies creating conditions under which the public can easily encounter (be proposed or offered) cultural content that is culturally relevant. In other words, the notion of discoverability includes the capacity to encounter local cultural content and content that is made in languages other than English. Discoverability, however, tends to function on algorithmic biases that privilege English-language cultural content and content produced by large global corporations. From a cultural policy perspective, discoverability is rooted in two basic dimensions: the regulation of culture and the accessibility of culture. This article emphasizes the place of French-language content and touches on two dimensions: the accessibility of French-language digital content in general, and the issue of cultural content from French-speaking minorities. In doing so, it also sheds light on strategies and policies that are pertinent to other languages and to other linguistic minorities.
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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 | 0.015 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".