Experience, the Name We Give Our Mistakes
Bibliographic record
Abstract
A long-awaited academic appointment has been obtained after seemingly endless years of medical and postdoctoral training: at first, it may seem as if the steep pathway to academia will finally start to balance out once this appointment begins.Soon enough a frontloaded schedule including first grant applications, clinical and educational commitments, a heavy stroke call schedule, and administrative requirements will make it clear that to reach the top there is still a steep road ahead.It is quite common to see newly minted clinician-scientists struggle to launch a research career while sailing through a series of brand new responsibilities that come with the new job.A novice approach to such challenges, particularly when mentorship is inadequate, can easily lead to physician burnout and in the worse case scenario, to failure to launch an academic career or to achieve promotion.In this InterSECT article, we aimed to describe 7 of the most common mistakes junior academics make and to provide strategies to navigate them effectively.
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 | 0.024 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.013 | 0.042 |
| Insufficient payload (model declined to judge) | 0.030 | 0.025 |
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".