Designing a Preparedness Model for the Future of Open Scholarship
Bibliographic record
Abstract
Over the next 12-18 months, leading structures relied on to conduct, publish and disseminate scholarly research are at risk of collapse. We believe that there's an urgent need to invest now in a coordinated approach to create a preparedness plan for the future of scholarship and research at the institutional level. In doing so, institutions have an opportunity to explore collectively cost-effective and sustainable solutions to address immediate needs at their institution. They also have an opportunity to play an active role in furthering a larger, more systemic shift towards open, community-owned and operated infrastructure at the institutional level to support scholarship and ensure research continuity. To support that shift, IOI has launched a research project in partnership with a network of institutional decision makers to model the future of open scholarship. This research is designed to address pending infrastructure consolidation and collapse across the research ecosystem, identifying the opportunities, leverage points, costs and approaches that could be employed to enable the following: * Creation of shared set of principles to help assess solutions based on a values-based framework; * Support that addresses heightened demands on universities as they shift operations online and transform the way they serve their communities; * Coordinated scenario planning that plans for a radical shift towards open scholarship and a convergence on existing, open tools and services; * Ways to pool resources and risk to maximize cost-effectiveness and minimize system failure; * Creation of a shared action plan to facilitate coordinated decision-making ensuring research continuity; * Bolster researcher productivity, continuity, and growth in both the near and long-term.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".