Bibliographic record
Abstract
emancipation? It is now upward of twenty-five years that I have been at university, as an under-graduate and master’s student, as a sessional lecturer, as a student again (this time in a PhD program), as a research assistant and, finally, as a professor. While I have never produced scientific work focused on this institution, my knowledge has been acquired through practical experience in France, Ontario and Quebec. Penning a piece entitled “The University System: Alienation or Emancipation? ” is thus an extensive exercise in self-reflexivity, more welcome in this setting than in university teaching and research. Here, in TOPIA, the researcher can feel free to carry along his or her subjective desires for reality, while in the confines of university one is restrained to other research subjects, forgetting “the difference between observa-tion and desire, between an objective assertion and a performative judgement” (Bourdieu 1997: 33). At the same time, pronouncing upon a subject on which I am surely no specialist is equally an exercise in freedom of speech, challenging the notion that “experts ” must be “recognized ” in their “fields. ” The hyper-specialization
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.547 | 0.229 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".