Modelling A Cooperative Approach to Open Access Scholarly Publishing: A Demonstration in the Canadian Context
Bibliographic record
Abstract
Background In light of increasing interest in open access publishing, this Research in Brief proposes and presents a financial analysis of a cooperative approach to moving subscription journals to open access.Analysis The article utilizes a 2014 survey of Canadian scholarly journals as well as an earlier 2004 survey to demonstrate the ways in which a cooperative model can mitigate publisher risk and sustain open access.Conclusions and implications The study sets out the financial details of moving the “average” Canadian subscription journal to open access with the support of its previously subscribing libraries, in ways that need not involve a publisher revenue loss or a library expense increase.Keywords Journals; Open access; Financial modelling; CanadaContexte Vu l’intérêt croissant pour l’édition à libre accès, cette Recherche en bref propose et présente une analyse financière d’une approche coopérative à bouger les revues d’abonnement à l’accès libre.Analyse Cet article utilise un sondage de 2014 des revues scolaires canadiennes ainsi qu’un sondage auparavant de 2004 à décrire les façons dont un modèle coopératif peut réduire le risque d’éditeur et maintenir l’accès libre.Conclusion et implications L’étude expose les détails financiers de bouger la « moyenne » revue d’abonnement canadienne à l’accès libre avec le soutien de ses bibliothèques qui lui s’abonnent précédemment, dans des façons qui n’impliquent pas une perte du chiffre d’affaires d’éditeur ou une augmentation de la dépense de bibliothèque.Mots clés Revues; Accès libre; Modélisation financière; Canada
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".