Postface: Differentiating and Differentiated Views on Linguistic Representation
Bibliographic record
Abstract
This special issue of the Canadian Journal of Linguistics/Revue canadienne de linguistique is devoted to the intriguing notion of représentation linguistique, an idea that developed in French (socio-)linguistics some ten years ago (cf. Jodelet 1993), following earlier work by Anne-Marie Houdebine (1982). In their preface, Philippe Hambye and Anne Catherine Simon discuss one of the major tenets of this approach, namely the idea that représentation linguistique, in addition to being concerned with how language is represented in the human mind — and thus very much in line with the cognitive turn of linguistics — also implies that as a cognitive entity, language has no self-contained status in the sense of post-structualist, generative grammar. Rather, the way in which we language users look upon language is always socially contextualised. Whatever we know, in the largest sense of the word, about language and varieties of language is inextricably linked to the situations and social groups, the speakers and geographical regions, the linguistic genres and social styles that constitute it. Linguistic representations, understood as everyday knowledge of language, are therefore normatively organised. They have to do with how someone of a certain type (incumbent to a certain social category) is supposed to, or can be expected to speak, given certain typified circumstances, and what it means for him or her to speak in this way.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".