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Towards a Genealogy of Academic Freedom in Canadian Universities1

2012· book-chapter· en· W6636775 on OpenAlexaffabout
Kenneth D. Gariepy

Bibliographic record

VenueSensePublishers eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAcademic Freedom and Politics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAcademic freedomDutyHigher educationLiberal arts educationPolitical scienceIntellectual freedomSociologyLiberal educationThe artsSocial sciencePublic relationsLaw

Abstract

fetched live from OpenAlex

That professors and students must have the freedom to pursue what interests them intellectually–no matter the degree to which critics from inside or outside the academy might consider such enthusiasms to be esoteric, impractical, irrelevant, unprofitable, or controversial–is arguably the dominant, commonsensical core of the discourse supporting institutionalized academic freedom in Canadian universities. Whether one adheres to ‘old-fashioned’ ideas about the purposes of higher education being to facilitate, through liberal arts programmes, broadly-based understandings about the natural and social worlds, with the aim of producing graduates who are prepared to participate in society as well-rounded, socially responsible citizens, or to ‘new-fashioned’ ideas about higher education’s duty to (re)produce, through professional, practice- and skills-based programmes, workers and consumers who are prepared to fully participate in a capitalistic, globalized, and highly competitive economy, intellectual freedom is the conceptual basis upon which participants of public higher education can justify their arguments about what it means to ‘educate’, to ‘get an education’, and to ‘be educated’ in academically ‘free’ institutions.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0290.042
Scholarly communication0.0130.005
Open science0.0020.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.030
GPT teacher head0.266
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
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 routes2
Has abstractyes

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