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Record W2952158612 · doi:10.1177/0891243219854436

Gender Differences in the Provision of Job-Search Help

2019· article· en· W2952158612 on OpenAlex

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.

Bibliographic record

VenueGender & Society · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPreferenceInequalitySet (abstract data type)Perspective (graphical)General Social SurveyPsychologyDemographic economicsSocial psychologyChinaSurvey data collectionEconomicsPolitical scienceComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

The existing literature has well studied the use of social contacts in job search, including gender inequality, in using social contacts. What is missing is the perspective of social contacts who help others find jobs. Using a large data set from the 2012 China Labor-Force Dynamics Survey, this study reveals significant gender differences in the provision of job-search help. Compared with women, men are more likely to provide job-search help and especially show a greater likelihood of exerting direct influence on the hiring process. While women are gender neutral in their choice of help recipients, men display a selective preference for helping other men. This men’s advantage of providing job-search help, especially influence-based help, and men’s selective preference for helping other men, imply another prominent gender inequality in informal hiring in the labor market. This study suggests several theoretical propositions to explain the revealed gender differences in both “whether to help” and “whom to help,” providing a starting point for further research.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.081
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.072
GPT teacher head0.259
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