Bibliographic record
Abstract
When was the last you cried? Maybe it was while you were watching a sad movie or when a loved one was leaving you or because you just felt lonely. The next thing you know, you have a lump in your throat, your eyes start to water and tears are running down your cheeks. Considering that crying is an important and common part of everyone’s lives, many of us know surprisingly little about it.À quand remonte la dernière fois que vous avez pleuré? Peut-être que c’était lorsque vous étiez en train de regarder un film triste ou quand un proche vous a quitté ou parce que vous vous sentiez seul. Tout d’un coup, vous avez la gorge serrée, vos yeux deviennent humides et les larmes commencent à couler sur vos joues. Comme pleurer joue un rôle important de la vie de tous, beaucoup d’entre nous savent étonnamment peu à ce sujet.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".