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Record W7096133113

Canadian Association of University Teachers

2012· article· en· W7096133113 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic Freedom and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)NegotiationWorkforceAcademic freedomCorporate governanceFreedom of associationAcademic communityProfessional association
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.253
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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