Bibliographic record
Abstract
An 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".