Development of a Short Version of the Jackson Career Explorer: The JCE Mini
Bibliographic record
Abstract
The purpose of the present study was to create a shorter and contemporary version of the Jackson Career Explorer (JCE), titled “The JCE Mini”. Utilizing archival data of people who had completed the JCE ( N = 3105), half of the sample was used to develop shorter, three-item scales, and the second half was used to validate the shorter measure (102 items) with additional self-report responses. The JCE Mini showed good internal consistency for most scales and good convergent validity with other career inventories. Correlations with a personality measure were consistent with previous research of the full JCE. In Study 2, a new scale was created to assess an interest in technological careers to improve the JCE Mini’s relevance to the current job market by testing six new items. In addition, we tested 20 new items in nine scales that were updated to reflect changes in the workplace and improve the psychometrics of the scales. The new sample ( N = 609) completed 102 items from the JCE Mini of Study 1 and 26 new items. Results suggest that the new JCE Mini, consisting of 105 items that assess 28 work roles or specific careers (one more than the original JCE), and seven work styles demonstrate good internal consistencies for the scales and good convergent validity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".