Standing on the Shoulders of Giants: Interpreting Reflective Practice in TESOL
Bibliographic record
Abstract
Reflective practice (RP) has become popular in many professions as a mark of professional competence. With this increase in popularity, many different definitions have developed alongside the many different approaches that have been advanced with many different theoretical underpinnings attached to these approaches. This is also the case within the field of education where although most educators agree some form of reflection is desirable, there is still no agreement on what RP is or how it should be implemented. RP has also been fully embraced within the teaching English to speakers of other languages (TESOL) and unfortunately with the same lack of clarity as to what it is or how it can be implemented. One reason for this lack of clarity it that we seem to have forgotten where it originated and why it made a resurgence much later. In this paper I look back at two of the giants of the RP movement and then outline how I have interpreted their work and my own work in the implementation of RP for TESOL teachers. I also look to the future (reflect-for-action) of RP for TESOL teachers
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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 | 0.065 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.075 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".