Justice and equity for whom? Reframing research on the “bilingual (dis)advantage”
Bibliographic record
Abstract
Abstract The search for the existence and nonexistence of bilingual advantages and disadvantages has become a battleground marked by polarized comments and perspectives, furthering our understanding of neither bilingualism as an experience nor cognition as higher-level mental processes. In this paper, I provide a brief historical overview of research examining the cognitive and linguistic consequences of multilingualism and address the assumptions underlying research exploring the bilingual behavioral difference. I aim to illustrate the sole focus on behavioral (dis)advantage fails to reflect the complexity and dynamicity of people’s bilingual experiences, thereby distracting from understanding bilingualism. Responding to the call of this special issue, I describe the necessity to focus on people when moving toward a just and equitable future for applied psycholinguistic research. Furthermore, I explain why the nuances of bilingualism need to be recognized beyond binary categorization to advance knowledge about bilingualism and its consequences. To avoid unjust misattribution of a behavioral outcome to people’s life experience and to report research findings in a transparent manner, the myopic representation of the terms “bilingual (dis)advantage” should be recognized and reflected on.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".