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Record W2331444068 · doi:10.1177/0165025414533222

Career pursuit pathways among emerging adult men and women

2014· article· en· W2331444068 on OpenAlexaff
Shmuel Shulman, Tamuz Barr, Yaara Livneh, Jari‐Erik Nurmi, Kati Vasalampi, Michael W. Pratt

Bibliographic record

VenueInternational Journal of Behavioral Development · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPsychologyGoal pursuitDiversity (politics)Developmental psychologyCareer developmentCareer PathwaysAdult developmentPersonalityClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The present study examined career pursuit pathways in 100 Israeli emerging adults (54 men) who were followed from age 22 to 29. Employing a semi-structured interview at the age of 29, participants were asked about current work and educational status, work and educational goals and status changes in recent years, and to reflect on the meaning of the processes they followed. Analyses of interviews yielded four distinctive career pursuit pathways that were associated with different levels of concurrent well-being: Consistent Pursuit, Adapted Pursuit, Survivors, and Confused/Vague. Self-criticism, efficacy, and level of motivation measured seven years earlier predicted pathway affiliation at 29. In addition, paternal support was found to serve as a protective factor associated with adaptive career pursuit. Gender differences were found, with women more likely to be affiliated with the less adapted pathways. In addition, paternal and maternal support were differently associated with career pathways. By employing this mixed-method approach, the findings demonstrate the diversity, and gender-related nature, of career pursuit and development pathways during emerging adulthood, and indicate the importance of personality and both paternal and maternal support in the process of career development.

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 score0.200
Threshold uncertainty score0.413

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 head0.030
GPT teacher head0.283
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

Citations16
Published2014
Admission routes1
Has abstractyes

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