Bibliographic record
Abstract
INTRODUCTION Nonnative English-speaking (NNES) educators constitute a large majority of English as a second or foreign language teachers around the world (Canagarajah 1999). However, it is only recently that they have become more visible and that the field of teaching English to speakers of other languages (TESOL) has begun to address issues that are of concern to them. Although there is wide agreement that the terms native and nonnative speaker are impossible to define (Kaplan 1999) and that they “obviously and pointlessly dichotomise the world neatly into ‘us’ and them” (Kaplan 1999: 5), the reality is that “teachers who are perceived as speaking a language other than English as their mother tongue – regardless of their actual proficiency with English – are typically labelled as ‘nonnative English speakers’” (Pasternak and Bailey 2004: 156). This chapter focuses on NNES educators in relation to issues of language teacher preparation programs. SCOPE AND DEFINITIONS It could be argued that work on NNES teachers-in-preparation has focused on two different broad themes related to the setting in which language teacher-education programs are offered. Specifically, the first theme deals with issues of teacher language proficiency in relation to language teacher-preparation programs. With some exceptions, this line of work has been the focus of attention in teacher-preparation programs for / in English as a foreign language (EFL) settings (e.g., Barnes 2002; Berry 1990; Chacon 2005; Cullen 1994, 2002; Lavender 2002; Murdoch 1994, etc.). Work on issues of language proficiency draws on the notion that “a teacher’s confidence is most dependent on his or her own degree of language competence” (Murdoch 1994: 258); therefore, it deals with language as a skill that needs to be improved for a teacher to be a successful professional. The second theme has mainly focused on issues of teachers-in-preparation in Inner Circle settings, where English is the dominant language (U.S., U.K., Canada, Australia, New Zealand); therefore, it may not be surprising that work in this area has dealt with how NNES teachers-in-preparation socialize into their language education programs in these countries, how they perceive themselves in relation to their English-speaking peers, and how they develop a sense of professional identity (e.g., Brutt-Griffler and Samimy 1999; Golombek and Jordan 2005; Morita 2004; Thomas 1999; Pavlenko 2003, etc).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".