Bibliographic record
Abstract
A n unscientific survey of the faces one passes on the campus of a large university like McGill, will allow for the hypothesis to be made that the student body is not happy.While some faces are animated with elation, and others seem still with contentment, others yet sag with exhaustion.Take this experiment one step further and discover that many eyes are glazed over with distraction; eye contact is avoided by some and intensely pursued with the searchingness of one lost by others.Engage a sample of students in conversation and the words tired, stressed, busy and frustrated are thrown around with frequency.Some may complain of too little sleep, others of too much drinking or eating, and still others yet neglect to take care of themselves at all.Seek out textual support in the form of the writing on bathroom stalls throughout the womens, and possibly also the mens, bathrooms.Here one can read anonymous correspondence that shares confusion, anger and hope regarding topics ranging from specific romantic relationships, to sexuality in general, and encompassing eating disorders, physical ailments as well as miscellaneous rants.The claim I make here is not that there are no happy students, but rather that there absolutely are unhappy ones.I will be addressing the phenomenon of unhappy undergraduate university students in this paper.I have wondered at the value of an education that leaves its student body in large part unhappy and confused
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.131 | 0.072 |
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".