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Record W4394959937 · doi:10.33423/jlae.v21i1.6912

The Ethics of Institutional Analysis: Paternalism and Proprietary Access to Canadian U15 Faculty

2024· article· en· W4394959937 on OpenAlexafffundabout
Taylor Ellis, Sandra G. Kouritzin, Satoru Nakagawa, Jason D. Edgerton, Merli Tamtik

Bibliographic record

VenueJournal of Leadership Accountability and Ethics · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPaternalismBureaucracyWorkloadPublic relationsPolitical scienceAffect (linguistics)PsychologyManagementEconomicsLaw

Abstract

fetched live from OpenAlex

Through an investigation of Canadian U15 faculty experiences with workload a common concern emerged regarding expansions to the bureaucratic and managerial functions of the university that negatively affect faculty members. These functions overlap with concerns about research ethics when Offices of Institutional Analysis (OIA) evaluate research projects, often justified as limiting faculty and student survey fatigue. Yet, secondary reviews by OIAs frequently manifest as additional ethical reviews, seeming to arise from a notion of paternalism whereby universities treat constituencies as property to be managed and controlled. Students, staff and faculty are constructed as being protected by this review process, framed as the University’s moral imperative. These bureaucratic add-ons negatively affect faculty, adding stress to initiating already complex research programs, thereby alienating research faculty. OIAs are normally established and governed by administrators and non-academic staff; they are, therefore, immune from direct faculty input and oversight. We raise concerns about institutional isomorphism, suggesting that discussion and possibly intervention are needed to prevent universal adoption of these processes throughout higher education.

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)0.008
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.268
GPT teacher head0.454
Teacher spread0.186 · 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.

Study designObservational
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
Published2024
Admission routes3
Has abstractyes

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