Bibliographic record
Abstract
Greetings from the Editor-in-Chief. To read the full message, please open the PDF link. I write my first editorial for the Canadian Journal of Career Development (CJCD) with a heaviest of hearts. In May 2024, the field of career development lost one of its greatest champions, Dr. Robert (Rob) Shea. There are no shoes or heart big enough to fill the void he leaves in the lives of his loving family, and all those who were fortunate to know and learn from him. In 1998, as a graduate student interested in pursuing a career in Career Development, Rob agreed to be my co-supervisor for my internship at Memorial University’s Career Centre. Little did I know then the impact that introduction would have on both my career and life. One of the first things he told me about when we met all those years ago was his vision for Canada’s first and only journal focused on Career Development. He was in the process of developing the original artwork for the cover, talking to different groups about funding, and getting people generally excited about what this could be. It is fitting that in his last editorial in the January 2024, Rob reflected on the Journal’s humble beginnings and stated, “... we began the journal as a field of dreams concept – 'Build it and they will come'” and like so many of Rob’s dreams, - they came true. You did come, and currently there are almost 16,000 subscribers worldwide and new subscribers steadily.
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 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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.374 | 0.368 |
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".