Perspectives on Academic Publishing: Can we Find Common Ground?
Bibliographic record
Abstract
Over the past two decades, the cost of electronic journals has continually grown to the point that many academic libraries today find it financially unsustainable. This has made it necessary for librarians to begin cancelling major journal subscription packages, known colloquially as big deal packages. The cancellation of big deal packages is complex, and at present, poorly understood practice revolving around the analysis of a range of quantitative and qualitative data. Moreover, it also has a distinct micro-political dimension as it involves reconciling the, at times, conflicting interests of faculty, students, librarians, and publishers. This panel seeks to initiate a dialogue between a diverse group of stakeholders with interests in this issue. It is expected that this dialogue will increase awareness of the complexity involved in managing the size, scope, and cost of big deal subscription packages and will give voice to multiple perspectives on the issue. As such, it is hoped that the panel will contribute to finding common ground from which we can collectively begin thinking about how the symbiotic relationship between academic libraries and publishers can be reinvented. Au cours des deux dernières décennies, le coût des revues électroniques n'a cessé d'augmenter au point que de nombreuses bibliothèques universitaires se trouvent aujourd'hui dans une situtation insoutenable. Cela a obligé les bibliothécaires à annuler les abonnements aux grands ensembles offert par les plus importants éditeurs commerciaux. L'annulation des grands ensembles de revues est une pratique complexe et, à l'heure actuelle, mal comprise, qui nécessite l'analyse de données quantitatives et qualitatives diverses. De plus, ces désabonnements ont une dimension micropolitique distincte car ils impliquent de concilier les intérêts parfois conflictuels des professeurs, des étudiants, des bibliothécaires et des éditeurs. Ce panel vise à initier un dialogue entre diverses parties prenantes de cette question. Il est attendu que ce dialogue suscitera une prise de conscience de la complexité la gestion de la taille, de la portée et du coût des grands ensemble et donnera la parole à de multiples perspectives sur cette question. Nous espérons que le panel contribuera à trouver un terrain d'entente à partir duquel nous pourrons collectivement commencer à réfléchir à la manière dont la relation symbiotique entre les bibliothèques universitaires et les éditeurs peut être réinventée.
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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 | 0.054 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.027 |
| Science and technology studies | 0.037 | 0.100 |
| Scholarly communication | 0.087 | 0.097 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.028 | 0.036 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".