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Record W2160605399 · doi:10.1348/096317909x474786

Career transitions and their causes: A country‐comparative perspective

2009· article· en· W2160605399 on OpenAlexaff
Katharina Chudzikowski, Barbara Demel, Wolfgang Mayrhofer, Jon P. Briscoe, Julie Unite, Biljana Bogićević-Milikić, Douglas T. Hall, Mireia Las Heras, Yan Shen, Jelena Zikic

Bibliographic record

VenueJournal of Occupational and Organizational Psychology · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsYork University
Fundersnot available
KeywordsChinaPerceptionAttributionPerspective (graphical)PsychologySocial psychologyBlue collarCareer developmentPolitical scienceDemographic economics

Abstract

fetched live from OpenAlex

This empirical paper investigates how individuals conceptualize causes of career transitions, focusing on the three European countries of Austria, Serbia, and Spain in comparison to the USA and China. Collectively, these countries represent four separate cultural regions according to Schwartz. Semi‐structured interviews were conducted with members of three occupational groups: business people, nurses, and blue‐collar workers. Analysis of the data generates greater insight about the existence of both region‐specific patterns as well as potentially universalistic tendencies regarding perceived causes of career transitions. Perceptions of internal (to the person) drivers of career transitions as activating forces are evident in all five countries. The overall results support contemporary notions of occupational careers that are highly individualized, a characterization strongly emphasized in the current career literature. In the European culture clusters, causes of career transitions are attributed internally and externally. China, representing the Confucian cultural region, stresses external causes for career transitions. By contrast, in the USA only internal attributions of causes are reported.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.144
GPT teacher head0.391
Teacher spread0.247 · 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

Citations69
Published2009
Admission routes1
Has abstractyes

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