Exploring Language Teachers’ Evolving Conceptualizations of Language Variation
Bibliographic record
Abstract
This paper examines conceptions that pre-service teachers of English bring to the term language variation, a crucial constellation of concepts in linguistics related to understanding the socioculturally variant nature of language. We review responses given to open-ended questions about language variation, focusing on statements made regarding this term at different points of professional development, looking particularly at how initial understanding of language variation evolves as a result of having multiple exposures to this linguistic concept across differing language courses. Survey questions related both to a definition language variation and to an assessment of the importance of this concept for the careers for which these respondents were preparing. Comparative content analyses of responses reveals that many pre-service teachers start their academic careers with differing preconceptions of language variation based on general use of the term. Though these pre-service teachers are sometimes reflective about aspects of variation, their early formulations are quite narrow in scope, often reflecting an incomplete or less sophisticated understanding of the term. Results suggest that, as these pre-service teachers extend their coursework, they also expand and refine their initial understanding of language variation, thereby gaining a discipline-specific and nuanced understanding of the term. Results also show broad appreciation for language variation, and development in the ability to articulate how awareness of variation might assist their teaching.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".