Canadian Association of University Teachers
Bibliographic record
Abstract
As collaborations between universities1 and external individuals and organizations (donors, corporations, governmental agencies and bodies, NGOs, and foundations) proliferate, it is vital to have a clear set of principles to protect academic integrity and the public interest. The following principles cover various major donor-institutional and inter-institutional collaborative agreements, ranging from individual donors providing funding for a university institute or centre to broad strategic alliances such as the University of Alberta’s $10-million collaboration with Imperial Oil. After each principle, some specification is offered to clarify the context and provide some parameters to guide policy development and practice in universities. While there can be real benefits to various donor agreements and collaborative arrangements, some have threatened or compromised core academic principles and the public missions of universities. This statement is intended to provide guidance and recommendations for: (a) universities in developing policies and procedures governing donor agreements and collaborations; (b) governance review, monitoring, and assessment of such agreements and collaborations; (c) faculty members and other members of the academic workforce in thinking through a range of fundamental professional responsibilities and rights that are implicated and affected by donor agreements and collaborations; and (d) academic staff associations in negotiating collective agreement provisions to protect the academic freedom and other academic rights of their members.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.330 | 0.112 |
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".