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Record W6962937210 · doi:10.17605/osf.io/tmhn3

Understanding Student Perceptions of the Characteristics of Men, Women and Managers

2018· other· en· W6962937210 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionStereotype (UML)Stereotype threatEducational attainmentWork (physics)Work experience

Abstract

fetched live from OpenAlex

The purpose of the study is to replicate and extend prior work by Virginia Schein with respect to gender stereotypes of managers. Since the 1970’s numerous studies have confirmed that students and workers hold the stereotype that successful managers have more male characteristics versus female characteristics. Recent research has suggested that these stereotypes are weakening for women but not for men. Despite the large number of studies, only one has been conducted in Canada and this was over 20 years ago (Orser, 1994). Therefore the main purpose is to assess student stereotypes of managers as they relate to gender. We will also explore moderators of these stereotypes such as: participant gender, major, income and work status/history of parents and educational attainment of parents. A second purpose of the study is to assess the impact of insufficient effort responding on the pattern of results discussed above. Researchers have become increasingly worried about this issue with on-line surveys as these are typically completed in an environment chosen by participants (i.e. at home, work, library, etc.). As a result participants may be unmotivated to respond to each question carefully, may respond to each question while engaged in other activities (e.g. multi-tasking), or may simply respond in a manner that presents them in a positive way. Therefore, the research questions are: do Canadian students hold similar gender stereotypes for managers as in other countries? To what extent does work, participant gender, income and work status/history of parents, major (primarily Psychology and Business), and educational attainment of parents moderate these stereotypes? Finally, we are also interested in the rate of insufficient effort responding and how it affects the psychometric characteristics of the stereotype measure and the relationships in the prior research questions.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.393

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.062
GPT teacher head0.356
Teacher spread0.294 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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