Emerging Business Models for Scholarly Journals: The Library Perspective
Bibliographic record
Abstract
University libraries have witnessed sweeping changes in scholarly publishing over the past decade. Digital publishing has put an end to print runs for many established journals while provoking an explosion of new electronic titles. Market consolidation has allowed a handful of large vendors to thrive while smaller publishers fight to stay afloat. Journals are scrambling to offer Open Access alternatives that comply with the policies of universities and funding agencies. Scholars are exploring unprecedented opportunities for self-publishing and self-archiving. In the face of tight university budgets, academic libraries are under pressure to demonstrate that the millions we spend on journal content meet our institutional objectives as closely as possible. To this end libraries are creating purchasing consortia, establishing Open Access author’s funds, implementing journal publishing systems, and engaging in initiatives to support the long term preservation of digital content. This session will discuss challenges and opportunities in the scholarly publishing landscape from the perspective of academic libraries. We will identify important publishing trends, discuss the pros and cons of different business models, and identify common objectives that may allow us to work together to create a strong, sustainable publishing model for independent Canadian journal publishers.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.067 | 0.028 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".