Future of Open Scholarship: Preliminary Findings
Bibliographic record
Abstract
This report shares a preliminary summary of the findings and top level insights of the Future of Open Scholarship stakeholder interviews, run by the authors from June 29 to August 24, 2020. Over 54 interviews were conducted (some individual, some group), with a total of 81 participants from 56 different institutions, scholarly societies, and supporting organizations. (An additional 18 participants as a part of this research effort who have not yet participated in an initial user interview at the time of this report). Engagement in this work involves representatives from 18 countries and 5 continents around the world. These include Egypt, Malaysia, Australia, New Zealand, Mali, Zimbabwe, Kenya, South Africa, Algeria, Sudan, Germany, the Netherlands, Belgium, France, Spain, the United Kingdom, Canada, and the United States. For more information on this project, including access to additional resources and reports, visit: https://investinopen.org/research/future-of-open-scholarship/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".