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Record W4400494321 · doi:10.1080/1359432x.2024.2376297

Alternative career pathways of skilled migrants: looking for new meanings amid starting again

2024· article· en· W4400494321 on OpenAlexafffund
Jelena Zikic, Soodabeh Mansoori, Viktoriya Voloshyna

Bibliographic record

VenueEuropean Journal of Work and Organizational Psychology · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsThompson Rivers UniversityYork University
FundersMitacs
KeywordsSociologyCareer PathwaysCareer developmentEconomic geographyEpistemologyLabour economicsGender studiesPsychologyEconomicsPedagogyOperations managementPhilosophy

Abstract

fetched live from OpenAlex

This qualitative study aims to provide an in-depth understanding of skilled migrants’ lived experiences of alternative careers. We explore identity work and meaning-making processes of career actors for whom alternative career options often meant “beginning again”. While focusing on psychological, temporal, and contextual dimensions of alternative career transitions, our findings identify three unique alternative career pathways: provisional, experimental, and reformist; each characterized by a unique form of identity work and accompanying types of meaning-making. We find that alternative career pathways differ in terms of their temporary and at times provisional nature as well as career actors’ (in)ability to engage in the present search for meanings and purpose in alternative careers. This study advances existing literature on major career transitions and specifically migrant career trajectories inside of local organizations and through unique forms of alternative careers. We also build on the existing meaning-making literature by highlighting the career narratives of those who must search for new meanings while pursuing “less than ideal” career opportunities. Finally, our findings provide practical implications related to outcomes of alternative career opportunities on migrant career success but also more broadly for employers and policymakers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.347

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 studies0.0000.000
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 head0.026
GPT teacher head0.252
Teacher spread0.226 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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