Migrant ethnic identities, mobile language resources: Identification practices of Sri Lankan Tamil youth
Bibliographic record
Abstract
Abstract In the context of multilingualism and migration, the place of ethnic identities has come into question. Applied linguists have to contend with the possibility that ethnic identities have to be redefined in the light of changing orientations in the field. The critique of essentialism, the dismantling of the ”one language = one community” equation, and the fluidity of translanguaging have raised the question whether ethnic identities can be treated as real anymore. In the face of these changes, one group of activist scholars invokes values of ecological preservation and language rights to insist on traditional ethnic identities. Another treats ethnic identities as transient and playful under labels such as ludic ethnicities and metroethnicities. Interpreting the identification practices of Sri Lankan Tamil diaspora youth in UK, USA, and Canada, this article argues for a middle position of strategic constructivism. That is, though the Tamil youth are multilingual, and do not claim full proficiency in Tamil language, they use their mix of codes to construct Tamil ethnicity in situated uses of their repertoire. The article argues that ethnicity should be treated as a changing construct, with different codes employed to index identity in changing times and places.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".