Insights and Perspectives from the PhD to Employee Forum
Bibliographic record
Abstract
Data shows that PhD graduates pursue diverse careers. Recent data from Canadian universities report that fewer than 35% of health-science PhD graduates are employed in research intensive, or tenure-stream, faculty positions up to seven years after graduation. Perhaps surprisingly, this is higher than previous estimates, which indicate that up to 80% of basic biomedical PhDs are employed outside of tenure-track positions within 6-10 years of obtaining their degree. The “From PhD to Employee Forum” was born out of a pressing need to identify specific solutions to manage the challenge of effectively engaging trainees in career development during their doctoral degree. To address this challenge, we sought to bring together career development experts to collect insights regarding the approaches of different institutions to address the career planning needs of life science trainees. Here we summarize key presentations at the forum, review what we see as some of the key challenges in the career preparation of life scientists and summarize three key insights raised in the forum.
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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.019 |
| Scholarly communication | 0.028 | 0.011 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".