Bibliographic record
Abstract
Deborah Cameron names and describes a familiar and time-honoured stream in the discourse on language: those value-laden prescriptions and proscriptions about language use and the debates they engender, what she calls "verbal hygiene."This genre includes those "practices ... born of an urge to improve or 'clean up' language" and constitutes "a single (and normative) activity: a struggle to control language by defining its nature" (p.8).As educators, we are familiar with the phenomenon of verbal hygiene through a number of its specific manifestations -the style guides we recommend to our students, literacy initiatives, assertiveness training, political correctness, linguistic equality, the plain language movement, employers' anxieties about our graduates' workplace writing skills, and our self-appointed correction of the grammar and style of others' documents.Many of us are also avid participants in other popular practices of verbal hygiene -perhaps as writers of letters-to-the-editor lamenting the decay of our language or lambasting a barbarous usage, or as anxious parents calling for grammar drill and a return to the good old days of standards and basic literacy.To some extent, we are all language mavens, partial to certain practices of verbal hygiene, sometimes throwing our own glove down in a battle over language use or change.Deborah Cameron isn't advocating against such practices.In fact, she wants to validate the concerns that lay people and non-linguists so passionately display about language, and to encourage linguists to take these concerns seriously.Cameron me-
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".