Bibliographic record
Abstract
The political theorizing of language is unavoidably reliant on at least certain basic assumptions concerning the nature of language and linguistic agency. In multilingual and multicultural societies such as Canada, the task of identifying, articulating, and ultimately evaluating such assumptions is more complex, given their more heterogeneous linguistic landscape, and the (sometimes conflicting) clusters of beliefs, attitudes, anxieties, hopes, and expectations attached by speakers to particular languages as well as to the broader repertoire. The chapter focuses its attention on the debate over multiculturalism/interculturalism in the Canadian context. It explores and defends the argument that this debate can be seen in fact as a debate between two distinct conceptions of language and linguistic agency, namely the designative (“Lockean”, i.e., language as detached from a partial and intersubjective human experience) and the constitutive (“Herderian”, i.e., language as inextricably linked to a contextualized social epistemology), respectively. The distinctive logic and reasoning of both models, the chapter argues, can only be defended by embracing a non-holistic “in-betweenness” experience (and conception) of language as an underlying constitutive commonality.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".