Bibliographic record
Abstract
September is here, and so is C&EN’s annual review of the employment market for chemical scientists. You can turn to page 26 to read stories of job seekers and employers in the academic sector in the US and Canada during the 2020–21 hiring season. If you are wondering whether the academic job market will bounce back after the pandemic, we unfortunately don’t have a satisfying or categorical answer. Tenure-track jobs are always highly coveted, and during the pandemic they have been more difficult to find. And hiring processes have been longer than usual, according to the people C&EN reporter Bethany Halford spoke to. By contrast, the pharma job market is superhot right now with, for example, an academic group leader reporting on Twitter that postdoctoral fellows are being snatched up after less than a year into their medicinal chemistry labs. Many of us remember when Big Pharma was downsizing not
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.143 | 0.117 |
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".