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Record W2623991035 · doi:10.17722/ijme.v8i3.904

Career Onion: Peeling off the layers for Occupational Preferences and Career Aspirations

2017· article· en· W2623991035 on OpenAlexvenueno aff
Samiah Ahmed, Alia Ahmed

Bibliographic record

VenueInternational Journal of Management Excellence · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
Fundersnot available
KeywordsMaslow's hierarchy of needsVocational educationPsychologyExpectancy theoryCareer developmentCareer counselingHierarchyCognitive Information ProcessingCareer portfolioSocial psychologyApplied psychologyPedagogyEconomics

Abstract

fetched live from OpenAlex

Vocational psychology carts away the vocational behavior regarding the occupational preferences of every individual, which commences at the period of adolescence. These occupational or vocational preferences are shaped or crystallized through career guidance and theories, which further help an adolescent to climb the career ladder towards achieving career aspirations and success. This research article, focuses mainly on five theories, self-concept development theory, valence-instrumentality-expectancy theory, theory of work adjustment , tournament theory and Maslow hierarchy of needs theory, which help the adolescents with the occupational preferences, assist in climbing the career ladder from growth stage to retirement stage, ultimately resulting in achieving career aspirations. Furthermore, researchers reveal the differences among these theories highlighting unique features of every theory in predicting occupational or career preferences. Researchers also draw the career onion, where every layer of the career onion depicts that every adolescent peels off each career layer (starting from the growth stage until he eventually peels off the last layer of the retirement age) to achieve career self-actualization.

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 score0.524
Threshold uncertainty score0.808

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 communication0.0000.001
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 head0.133
GPT teacher head0.342
Teacher spread0.209 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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