Le crowdsourcing scientifique et patrimonial à la croisée de modèles de coordination et de coopération : Le cas des herbiers numérisés/Scientific and Heritage Crowdsourcing at the Crossroads of Models of Coordination and Cooperation: The Case of Digital Herbaria
Bibliographic record
Abstract
Nous etudions les dispositifs participatifs developpes par les institutions impliquees dans la conservation des herbiers : outre la consultation facilitee des collections numerisees, certains d’entre eux offrent la possibilite de participer a la transcription des informations contenues dans les images issues de la numerisation des herbiers, ou de proposer des corrections aux donnees deja saisies. Nous combinons une typologie des dispositifs de mediation et des regimes de l’action collective pour apprehender les specificites fonctionnelles de ces sites. Nous mettons en evidence la superposition de fonctions relevant de modeles de coordination et de cooperation differents, entre organisations anonymes et communautes, qui entrent parfois en contradiction. Abstract: We studied the participation mechanisms developed by the institutions involved in the conservation of herbaria. Besides the facilitated consultation of digitized collections, some of the mechanisms offered the opportunity to participate in the transcription of the information contained in the images from the digitization of the herbaria or propose corrections to data already entered. We combined a typology of mediation schemes and of modes of collective action to apprehend the functional features of these websites. We highlighted the overlapping of functions belonging to different coordination and cooperation models among anonymous organizations and communities, which sometimes come into conflict.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".