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Record W2052280380 · doi:10.1177/1356389012461192

The ethical sensitivity of evaluators: A qualitative study using a vignette design

2012· article· en· W2052280380 on OpenAlex
Geoffroy Desautels, Steve Jacob

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEvaluation · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité Laval
FundersGovernment of Canada
KeywordsVignetteContext (archaeology)PsychologyQualitative researchQuality (philosophy)Social psychologyEngineering ethicsSociologyEpistemologySocial science

Abstract

fetched live from OpenAlex

Evaluation occurs in a context fraught with ethical issues. Evaluators are regularly faced with ethical tensions that are likely to influence the quality of their work. By developing an analytical model that categorizes evaluators along an altruistic-corporatist axis, we sought to understand which factors influence the ethical sensitivity of evaluators. To this end, utilizing a qualitative research design using vignettes, we exposed a dozen Canadian evaluators to ethically problematic situations to examine their ability to identify the ethical issues within the scenarios. The ensuing results allowed us to conclude that an evaluator’s ethical sensitivity is partially explained by the altruistic or corporatist nature of that evaluator; it also depends on other factors such as knowledge of the prescriptive norms in ethical matters, experience conducting evaluations, and the milieu and the working conditions within which the evaluators operate.

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.

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)0.264
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2640.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.612
GPT teacher head0.648
Teacher spread0.036 · 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