Bibliographic record
Abstract
Research on teaching and teachers in the field of general education has refocused somewhat over the recent past to what teachers actually do. In other words, research has started to examine the different ways in which experienced teachers understand their practice in relation to their accumulated career experiences by listening to their voices and getting their views (Hargraves, 1996). This research with rather than on teachers now includes the teachers’ understandings of their profession with the idea that teachers can be generators of research rather than always being consumers of research by others (see also research approach later in this chapter). In English language teaching, Freeman (1996) has pointed out the importance of listening to teachers’ voices about what they do because he says that it is necessary to put teachers at the centre of telling their stories. Freeman (1996: 89) maintains that putting teachers in front and centre in terms of listening to what they do actually follows the jazz maxim: “You have to know the story in order to tell the story”. That said, not much has really happened in the English Language Teaching (ELT) field as we have not heard many of the voices of experienced English as a Second Language (ESL) teachers and their various experiences over their years of teaching ESL.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
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".