Attitudes to speech styles and other variables: communication features, speakers, hearers and contexts
Bibliographic record
Abstract
Much of what we have looked at so far has concerned attitudes to ‘whole’ languages (e.g. the French and English languages in Canada) and to social and regional accents within a language. In places, findings showed that such attitudes can vary amongst people of different ages or from different regions, or depending on the situation in which language is used. Moreover, language also comprises more features than regional or social accents, and people have attitudes towards these too. It is also reasonable, as such a field of research develops, for people to ask ‘does it make any difference if X?’, or ‘surely it will depend on Y.’ Communication processes are complex. In this chapter, we look at evaluative reactions to some other components of communication, and to some of the relationships between, and relative potencies of, some of these components. Matched and verbal guise techniques, along with the use of scales enabling the use of inferential statistics, have been particularly prominent and productive in attempts to examine relationships in this area. While coverage cannot be exhaustive here, in this chapter I seek to give a reasonable overview of some of the main work regarding communication features, speaker variables, hearer variables and contextual variables. COMMUNICATION FEATURES Lexical provenance Against the backdrop of research showing how people react evaluatively to the accent in which a message is delivered, Levin, Giles and Garrett (1994) compared the effects of the vocabulary used.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".