Un accès payant pour des ressources éducatives en licence Creative Commons : un paradoxe étudié au prisme d’une étude de cas, Faq2Sciences
Bibliographic record
Abstract
Le projet Faq2Sciences porté par l’Université Numérique Thématique Unisciel représente une banque de plusieurs milliers d’exercices en sciences naturelles, mathématiques et informatiques, alimentée par de nombreuses institutions d’enseignement supérieur. Il s’inscrit dans une logique de mutualisation des ressources éducatives libres (REL) de l’enseignement supérieur français, problématique qui a connu une recrudescence d’intérêt à l’occasion des confinements qui eurent lieu durant la pandémie de COVID-19. A travers une analyse quantitative des métadonnées associées aux exercices menée notamment au prisme de la théorie de l’échange social, nous montrons que prédominent les licences Creative Commons, alors même que l’accès plein et entier à ces ressources implique pour les institutions concernées de s’affranchir d’un abonnement annuel. Nous nous basons sur cette étude de cas pour discuter d’un paradoxe : le développement de ressources pédagogiques sur lesquelles sont apposées des licences de libre diffusion, mais qui n’en demeurent pas moins relativement difficiles d’accès dans la mesure où un paiement est demandé. Loin d’être un cas isolé en France, ce projet révèle certaines des difficultés que rencontrent les institutions qui veulent concilier impératifs économiques et idéaux des licences libres. Faq2Sciences represents an exercise database in natural sciences, mathematics, and computer science; it is designed by a consortium of French institutions of higher education. The mutualization of open educational resources (OER) at a national scale has seen a resurgence of interest during the lockdowns which took place during the COVID-19 pandemic, and discourses promoting open licenses for educational content have gained momentum. However, they are hindered by the fact that such initiatives need to be economically sustainable. In the present article, we use Faq2Sciences to illustrate this contradiction through a quantitative analysis of the metadata associated with the exercises. The fact that public institutions broadcast resources with open licenses, but ask for a fee to provide access to the database due to economic imperatives has become a growing paradox in France. As it hampers the development of a culture of open education, it needs to be addressed by the research community. Through the lens of the social exchange theory, notably, we analyze why content designers favor different types of Creative Commons licenses, while at the same time the overarching institution, Unisciel, requires a subscription to access the content.
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 | 0.036 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".