Bibliographic record
Abstract
I was very pleased to read Mari Haneda's response to my article, not simply because it comments positively on my work, but because I see Haneda's views as a useful contribution to a much-needed conversation on how to attend to culture in adult second-language classrooms at this time of recon-ceptualization of the place of culture in language education (Byram & Flem-ing, 1998; Courchene, 1996; Kramsch, 1993). Haneda brings hopeful possibilities into the following two facets of the culture exploration approach I advocate: language learners as ethnographers and complexity as an in-herent aspect of culture. As I agree with most of Haneda's views, I comment only on aspects where I feel I may add to the ideas she presents. Drawing on the ethnographic literature, Haneda observes that ethnog-raphers can play various roles on the observation-participation continuum in a given context. I fully agree and would like to add that the roles student participant-observers can play in a situation, especially outside the class-room, depend very much on the access they have to a given activity. In this
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".