Bibliographic record
Abstract
Greetings from the Editor-in-Chief and the Associate Editor. To read the full message, please open the PDF link. Welcome to the final issue of 2023 for the Canadian Journal of Career Development. This year has seen an increase in submissions to our Journal and we are grateful to have so many consider us for their research publication. Our reviewers were certainly delighted and intrigued by your work. Contained within this issue are six articles that address various topics related to career development. There are four articles that are published under our Peer-Reviewed Article Category, and two of which we are happy to have from our francophone community. There is one article published in our Practitioner & Community Best Practices and one article published in our Graduate Student Research Brief Sections. Each article is interesting, and we highly recommend you take time readingthem.
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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 | 0.011 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.062 | 0.069 |
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".