Bibliographic record
Abstract
Finding an academic job in chemistry is always challenging, with far more job seekers than available faculty positions. The COVID-19 pandemic made the process of getting one of those coveted professor positions even more difficult: fewer academic jobs were available than in years past, candidates had to interview remotely, and the entire hiring process took months longer than usual. Read on for stories of academic job seekers and employers in the US and Canada during the 2020–21 hiring season. Lorenzo Mosca has dreamed of becoming a chemistry professor for more than a decade, ever since he was a graduate student in Italy. As someone who enjoys teaching and research equally, he thought academia would be a perfect fit for him. He could mentor students and continue to make advances in his field of organic materials. Without question, he said, an academic career has always been plan A. When C&EN first
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.081 | 0.033 |
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".