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Record W2162900929 · doi:10.1177/0894845305279168

Junior High School Students' Career Plans for the Future

2006· article· en· W2162900929 on OpenAlexaffabout
Angela D. Bardick, Kerry B. Bernes, Kris Magnusson, Kim D. Witko

Bibliographic record

VenueJournal of Career Development · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCareer planningWork (physics)Plan (archaeology)Career portfolioMedical educationCareer educationPsychologyCognitive Information ProcessingCareer developmentPedagogyVocational educationMedicineEngineering

Abstract

fetched live from OpenAlex

This study uses the Comprehensive Career Needs Survey to assess the career plans of junior high school students in Southern Alberta, Canada. Junior high students are asked (a) what they plan to do after they leave high school; (b) their confidence in finding an occupation they enjoy, obtaining training or education, and finding work in their chosen occupation; (c) how important it is to work in their community; and (d) where they anticipate working. Results indicate that junior high students intend to pursue further education and work and are optimistic about achieving their career goals. Students would like to find employment in their community but are considering working provincially, nationally, or internationally. Results suggest that career planning programs need to begin at the junior high level and that junior high students need to be involved in career program planning and future needs assessments.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.414
Threshold uncertainty score0.823

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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

Citations37
Published2006
Admission routes2
Has abstractyes

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