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Record W1482497299 · doi:10.1108/17542410810878068

Gender differences in involuntary job loss and the reemployment experience

2008· article· en· W1482497299 on OpenAlexaff
Jelena Zikic, Ronald J. Burke, Lisa Fıksenbaum

Bibliographic record

VenueGender in Management An International Journal · 2008
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsYork University
Fundersnot available
KeywordsOriginalityPsychologyPersonalitySample (material)Job lossSign (mathematics)Value (mathematics)Social psychologyApplied psychologyUnemploymentComputer science

Abstract

fetched live from OpenAlex

Purpose The study seeks to compare the experiences of job loss and reemployment experiences among female and male higher level managers and professionals. Design/methodology/approach The paper compares data collected at two periods in time from ( n =120) females and ( n =184) males who completed two self‐report questionnaires. Findings Relatively few gender differences were observed in the present study. The fact that females and males experienced the job loss and re‐employment process similarly was interpreted as a sign of progress. Main differences were found in networking and personality types, with men being more successful in networking and less agreeable types. Research limitations/implications This is a self‐report study and somewhat smaller sample at time two. Secondly, some of the findings may not generalize to those outside of outplacement. Practical implications Outplacement services may use these findings in guiding their counseling practice and focusing more on helping female executives in their networking efforts for example. Originality/value This paper contributes to the gender literature by looking at experience of job loss and reemployment for a particular and rarely examined group of individuals. It offers new knowledge on gender differences among executives and higher level managers.

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)0.001
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 score0.031
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.155
GPT teacher head0.408
Teacher spread0.253 · 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 teacher head, 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

Citations11
Published2008
Admission routes1
Has abstractyes

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