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Record W2767911308

"Without trust, research is impossible": Administrative inertia in addressing legal threats to research confidentiality

2017· dissertation· en· W2767911308 on OpenAlexfundaboutno aff
Aaren Ivers

Bibliographic record

VenueSummit (Simon Fraser University) · 2017
Typedissertation
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConfidentialityPolitical scienceInternet privacyPublic relationsPublic administrationComputer securityBusinessLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

The last two decades have seen the development of formalized federal research ethics policies in countries such as the US, Canada, Australia, and New Zealand. The focus of these policies has been researchers; comparatively little attention has been paid to the university administrations who provide the context in which those review bodies operate and whose resources are integral to protecting research participants when external threats arise. Far from being staunch defenders of academic freedom and protecting those who participate in research, university administrators in Canada have more commonly revelled in "edgy" research until the subpoena arrives, and then promptly thrown the researchers under the proverbial bus. In Canada, the federal ethics policy now requires university administrations to "support" their researchers when a legal threat arises, and "encourages" them to have policies in place that articulate how they will do so. Two years later, few policies exist. This thesis will review the record of administrative support for cases where research confidentiality is threatened, and present the results of a national survey of REB chairs, administrators, and REB staff, as to the current state of these policies and the impediments to their creation.

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)0.013
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.015
Insufficient payload (model declined to judge)0.0020.001

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 head0.527
GPT teacher head0.582
Teacher spread0.055 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2017
Admission routes2
Has abstractyes

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