Bibliographic record
Abstract
Christine Le thought she wanted to be a doctor, but she loved chemistry. When she had to decide on an undergraduate specialization, a professor took her aside for a chat. “He said, ‘People take a lot of different paths, so you should study what you love and let the career decisions unfold later,’ ” Le recalls. She ended up completing a doctoral program in organic chemistry and is now a postdoctoral researcher at the University of California, Berkeley. She’s now on the hunt for an academic job, hopefully in her home country of Canada. Le reads blogs, subscribes to alerts, sends emails to previous supervisors, and tries to get the word out about her search. “It’s extremely stressful,” she admits. “Right now, the postings are coming up and the cycle is just beginning, so I’ve been trying to formulate application packages, tidy up my CV, and think about job prospects—all while
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.116 | 0.081 |
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".