Bibliographic record
Abstract
When you call train information in Toronto, the automatic answering tape says HERE VIA RAIL/IC! VIA RAIL, underlining the cernpany's wish to present itself as a truly narional enterprise in a bilingual nation. In th e same way the national government in Canada is carefut (O preserve neutrality, in its policies and publications, with respect to bath the Engiish-speaking and the French-speaking population. The way in which neutraliry is achieved is by using bath languages, but this language doubling is but one ofthe strategies that cao be employed to be neutral. This chaprer is devoted (O a more systematic exploration of these strategies. The term ‘strategy of neutrality’ was introduced into sociolinguistics by Scotton (1976), who described intertribal interaction in urban Africa. Be10w we return in more detail to her research. Here we wiJl use ‘neutrality’ in rather loose sense and perhaps ambiguously, to describe two types of neutrality: - neurralization in in-group communication, which may be schematically represented as: (I) A/B → X ← AlB. In this type of neutralization, a ‘neutral’ communicative mode expresses a group's mixed ethnolinguistic identity. - neutralization of the communicative mode in situaticns of intergroup communication in which two groups ofspeakers with clearly separate ethnolinguistic identities do nor speak the same language. This type of neutralization may be schematically represented as: (2) A → X ← B Here X refers to the strategy ofneutrality, andA and B to the languages and identities ofthe speakers involved in the interaction. This second interpreration of neutrality is the one intended by Scotron (1976). In this chepter we wil! descrtbe the strategies in terms of these two types: neutralization of identity in section 11.1, and neutralization of communicative mode in 11.2, before anempting a more general perspective on neutraliry in sociolinguistics, in section 11.3. We should say right away that the way ‘neutrality’ was used in chapter 10 on code switching was rather different. There we referred to rhe points of neurrality between the structures ofthe two languages involved in code switching, a farm of grammatical neutrality.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".