Bibliographic record
Abstract
Language is obviously a power issue; at first political, but it is also symbolic and economic. We wanted to bring to light the problematics that reunite language and power, or better still languages and powers, thematic with which we were dealing during this day that has rallied linguists and literature specialists. In the context in which we were interested, that of Québec and francophone Canada, the link between language and power is so strong that it leads to the creation of neologisms, as Langagement, invented by Lise Gauvin to illustrate that Québecʼ engaged literature (even all Québecʼ literature) goes together with the issue of language. In these conference proceedings, we take as examples many discursive spaces, such as literature, dictionaries and the social discourse, fields where we can identify the strong relationship between language and power. With the contributions of Paola Puccini, Wim Remysen, Cristina Brancaglion, Nadine Vincent, Annette Boudreau, Isabelle Kirouac Massicotte and Marco Modenesi.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.016 |
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".