A Mind-Body Problem? A Reply to Lisa Taylor's Article "Canadian Culture," Cultural Difference, and ESL Pedagogy
Bibliographic record
Abstract
I would first like to thank Lisa Taylor for joining this debate. In her excellent article she has clarified a number of issues and, as any good author does, raised a number of important questions. In this short response I would like to comment on the distinction she makes between cultural diversity and cultural difference after Bhabha (1995). Taylor points out that with my insistence on the importance of know-ledge in the teaching of cultural (and, I believe, being a member of a cultural community) I represent the cultural diversity position. Although this may be true, what bothers me more is that we will end up seeing these two categories much like the mind-body problem in philosophy. For me the two are not separate; they are overlapping, mutually supporting positions. When new Canadians arrive in Canada, their point of reference is their own culture---a dynamic force that has shaped their lives since birth, an amalgam of experience and knowledge (see Figure I, Schein, 1985, p. 22). They may have little or no knowledge of the traditions and ritual, history,
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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.041 | 0.059 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".