The challenges and opportunities of an open future for small publishers
Bibliographic record
Abstract
Many small publishers, such as university presses, society publishers, and not-for-profit publishers, are taking steps to prepare their journals for the transition to open access. However, the path to open is not always clear and there is plenty of risk involved in the transition. This is particularly true for small-scale publishers with thin operating margins that stand to lose the most. Conversely, being unable to make a successful transition to open access is even more concerning as the scholarly publishing world shifts rapidly in this direction and those that fail to join the movement may be left behind. Canadian Science Publishing is an independent and not-for-profit scholarly publisher, and we are committed to transitioning our journals to open access. We are preparing for an open access and open science future by developing partnerships, implementing journal strategic plans, and rethinking our approach to scholarly publishing. We have been making progress towards our goals, but we have also encountered challenges and learned important lessons along the way. In the spirit of openness, we would like to be transparent about these challenges to help other small publishers learn from our experiences, both positive and negative. It is our hope that the presentation stimulates discussion and provides insight for small publishers that are pursuing an open future.
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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 | 0.063 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.078 | 0.070 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".