Bibliographic record
Abstract
You just got promoted to Associate Professor. Like most things in life, whether joys or sorrows, the joy of this accomplishment will not last forever. However, that doesn't mean that you should not look back and reflect on years of hard work and tenacity that you have put in which have earned you this promotion, so first of all, congratulations! Take a moment to savor this accomplishment. On the other hand, it would be a mistake to not ask the question, what just changed about me. Let's see. You now have tenure and you have been promoted to a senior rank. In one sense, that translates to less stress, but in another, you do have to wonder whether it necessarily does mean less stress. On the flip side, you should also take advantage of the opportunity to ask, what are some new freedoms I have just earned. The stress component is driven by partly knowing, but also partly being unsure of, the expectations from a newly minted Associate Professor. The freedom component stems from knowing that you are now tenured, which hopefully means that you can embark on more daring, high risk projects, even if you don't feel like you know quite how to negotiate the trade-off between risk and impact.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.468 | 0.255 |
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".