Professors Want to Share: Preliminary Survey Results on Establishing Open Source Endowed Professorships
Bibliographic record
Abstract
Abstract This study proposes a novel policy to provide incentives for open science: to offer open source (OS) endowed professorships. To hold an open source endowed chair, in addition to demonstrated excellence in their field, professors would need to agree to: 1) ensuring all of their writing is distributed via open access in some way, and 2) releasing all of their intellectual property in the public domain or under appropriate open source licenses. The results of this survey study of university professors in the U.S. show that a super majority (86.7%) of faculty respondents indicated willingness to accept an OS endowed professorship, while only 13.3% of respondents would not be willing to accept the terms of an OS endowed professorship. The terms of accepting an OS endowed professorship that were the most popular among respondents were increased salary, annual discretionary budget, as a term of tenure and annual RA or TA lines. Although it should be pointed out that more than a quarter of respondents would require no additional compensation. The results demonstrate a clear willingness of academics to expand open access to science, which would hasten scientific progress while also making science more just and inclusive. It is clear that science funders have a large opportunity to move towards open science by offering open source endowed chairs.
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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 | 0.035 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".