“I Got the Point Across and That is What Counts”. Transcultural Versus (?) Linguistic Competence in Language Teaching
Bibliographic record
Abstract
This paper examines the larger inquiries into post-secondary language instruction and the recommendations for curricular reform set out by the Modern Language Association’s Ad Hoc Committee on Foreign Languages, with a specific case study of the lesscommonly-taught language Hindi-Urdu. At York University, HindiUrdu is taught primarily (but not exclusively) to heritage learners at three levels. In building a relatively new program, I have faced several challenges that seem to be located at the intersection of transcultural and translingual as well as innovative and traditional approaches to language teaching. While I certainly do not hold an instrumental view of language learning and how it may relate to graduate studies (i.e.,preparing students for upper level literature, culture courses and/or archival work, etc.), I would like to discuss the practical side of an “intellectually and culturally informed” language pedagogy and its ramifications for language assessment.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".