“Heritage” learners of Hindi as a Foreign/Second Language: Motivation, Culture and Identity
Bibliographic record
Abstract
The surge of Heritage learners in Hindi as a foreign/second language classes in North American universities is the result of combination of several factors.A large-scale migration from South Asian Countries to North America has played a key role in raising the profile of South Asian related courses in universities across North America.The next generation of diasporic South Asians who come to universities for higher education try to negotiate their identity in the western world through culture and language.The unique flexible education system in American universities also allows the students to chose courses from a wide range of subjects in the Humanities.The students regardless of their selected major or minor have to fulfill requirements for taking course from Humanities and Social Sciences.Many Universities even have language requirements too.This unique feature of the education system gives the diaspora students an opportunity to venture into their heritage past and motivates them to take Hindi to fulfill the language requirement and also to make connection with their ancestral past through language.In this paper I will try to examine Hindi second language courses through their ethnic make-up i.e.Heritage and non-Heritage learners, and student motivations for learning Hindi. .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".