Reconstructing heritage language: resolving dilemmas in language maintenance for Sri Lankan Tamil migrants
Bibliographic record
Abstract
In recent years, Sri Lankan Tamils have fled their homeland as refugees as a result of the ethnic conflict in the country. Despite their heightened linguistic consciousness, community elders claim that Tamil youth are turning their backs on their heritage language. My data from Lancaster (California, US), East London (UK) and Toronto (Canada) shows a more complex attitude towards language maintenance by Tamil youth. Though a majority of the youth declared that English was their dominant language of proficiency, they insisted that it did not affect their positive orientation to ethnic identity and community affiliation. They adopted diverse language practices to enjoy in-group identity: namely, code switching into Tamil; emblematic uses of Tamil; switches into Tamilized versions of English; receptive competence in Tamil which enabled them to respond in English; and ritualized practices of communication where they could participate in communicative events with the aid of multimodal resources. These practices suggest that migrant Tamils are treating languages as fluid resources for identity and community construction. The hybrid and multilingual construction of heritage language and identity enables Tamils to shuttle between different languages and communities in migrant settings to resolve the dilemmas of mobility and identity.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".