Canadian professors’ views on establishing open source endowed professorships
Bibliographic record
Abstract
To accelerate scientific progress by advancing the spread of open access and free and open source software and hardware in academia, this study surveyed university professors in Canada to determine their willingness accept open source (OS) endowed chair professorships. To obtain such an open source endowed chair, in addition to demonstrated excellence in their field, professor would need to agree to ensuring all of their writing is distributed via open access and releasing all of their intellectual contributions in the public domain or under OS licenses. Results of this study show 81.1% Canadian faculty respondents would be willing to accept the terms of an OS endowed professorship. Further, 34.4% of these faculty would require no additional compensation. Respondents that favor traditional rewards for endowed chairs were shown to greatly favor receiving funds that would help benefit research (28% for graduate assistants to reduce faculty load or 46.7% for a discretionary budget-the most common response). These results show that, in Canada, there is widespread shared sentiment in favor of knowledge sharing among academics and that open source endowed professorships would be an effective way to catalyze increased sharing for the benefit of research in general and Canadian academia in particular.
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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".