MétaCan
Menu
Back to cohort
Record W1861942096 · doi:10.47678/cjhe.v30i1.183347

A Scale to Assess Student Perceptions of Academic Climates

2000· article· en· W1861942096 on OpenAlexafffundvenue
Teresa Janz, Sandra W. Pyke

Bibliographic record

VenueCanadian Journal of Higher Education · 2000
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsScale (ratio)AlienationPsychologySocial psychologyClassroom climatePerceptionReliability (semiconductor)Organisation climateHigher educationValidityMathematics educationDevelopmental psychologyPsychometricsGeographyPolitical science

Abstract

fetched live from OpenAlex

Sandler and Hall (1986) define a chilly academic climate as the "... subtle ways women are treated differently — ways that communicate to women that they are not quite first-class citizens in the academic community" (p. 1). This paper describes the construction of a scale to assess university students' perceptions of the chilly climate. An initial pool of 123 items was refined based on statistical analyses of the responses of 192 students to produce a 28-item Perceived Chilly Climate Scale (PCCS). Factor analysis identified five factors: Climate Students Hear About; Sexist Treatment; Climate Students Experience Personally; Classroom Climate; and Safety. To investigate further the reliability and validity of the scale, the PCCS, an Alienation Scale (Dean, 1961) and the Marlowe-Crowne Social Desirability Scale (Reynolds, 1982) responses were gathered from 327 students. As expected, the PCCS was significantly related to alienation but unrelated to socially desirable responding. Additional evidence supporting the reliability and validity of the PCCS is presented.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.079
GPT teacher head0.483
Teacher spread0.404 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations34
Published2000
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Higher EducationSame topicCommunication in Education and HealthcareFrench-language works237,207