Bibliographic record
Abstract
Cet article examine les faons dont les centres d'archives amricains tentent de contrler davantage l'utilisation de leur contenu en ligne, les raisons pour ce faire et le rle du droit d'auteur dans cette pratique.Dans une tude base sur 96 sites web de centres d'archives, 66 rponses un sondage et 8 entrevues avec du personnel, les donnes rvlent que les institutions se servent de mesures techniques pour limiter la qualit des images ou prvenir le copiage et tablissent aussi des conditions qui rgissent les autres utilisations.Dans certains cas, les centres d'archives peuvent agir pour protger leur lgitime droit d'auteur, mais dans la plupart des cas, les centres d'archives ne sont pas les dtenteurs de ce droit.Malheureusement, les conditions d'utilisation mises en place sont souvent lies au droit d'auteur, mme si l'intention des moyens de contrle est d'assurer l'attribution, de gnrer des revenus ou de garder trace de l'utilisation.Les centres d'archives devraient rexaminer leurs politiques sur la rutilisation de leurs fonds et collections afin de s'assurer qu'ils n'voquent pas le droit d'auteur de sorte restreindre l'utilisation du patrimoine documentaire en ligne.ABSTRACT This article examines the ways in which American archival repositories attempt to control further uses of their online content, their reasons for doing so, and the role of copyright in such practices.In a study based on 96 repository websites, 66 survey responses, and 8 interviews with staff, the data revealed that institutions use technical measures to limit image quality or prevent copying and also establish terms and conditions that govern further uses.In some cases, a repository may be protecting its legitimate copyright interests, but in most other cases the repository is not a rights holder.Unfortunately, conditions placed on further uses are often linked to copyright, even though controls are intended to ensure attribution, generate revenue, or track use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".