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Record W3047011350

Rival Structures for Career Anchors: An Empirical Test of the Circumplex

2016· article· en· W3047011350 on OpenAlexaff
Éric Gosselin, José Bélanger, Thierry Wils

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité de MontréalUniversité du Québec en OutaouaisHEC Montréal
Fundersnot available
KeywordsQuadrant (abdomen)Competence (human resources)BureaucracyExpansivePsychologyAutonomySocial psychologyEmpirical researchEpistemologyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

The way of organizing career anchors according to a circular logic has recently given birth to several competitive structures that did not receive empirical support. Unlike these structures of career anchors that were atheoretical, a new theoretical structuring model is proposed. Using the statistical technique developed by Browne (1992), it turns out that the theoretical model is superior to other structures. The results are also consistent with a structure based on quadrants of compatible career anchors. The careerist quadrant consists of anchor management; the protean quadrant respectively combines the technical/functional competence of anchors, creative challenges, entrepreneurship and autonomy/independence. The social quadrant brings together lifestyle as well as service/dedication anchors while the bureaucratic quadrant refers to the security/stability anchor.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.085
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0020.009
Scholarly communication0.0050.009
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0250.001

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 head0.054
GPT teacher head0.285
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2016
Admission routes1
Has abstractyes

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