Rethinking multilingual experience through a Systems Framework of Bilingualism
Bibliographic record
Abstract
Abstract In “The Devil's Dictionary”, Bierce (1911) defined language as “The music with which we charm the serpents guarding another's treasure.” This satirical definition reflects a core truth – humans communicate using language to accomplish social goals. In this Keynote, we urge cognitive scientists and neuroscientists to more fully embrace sociolinguistic and sociocultural experiences as part of their theoretical and empirical purview. To this end, we review theoretical antecedents of such approaches, and offer a new framework – theSystems Framework of Bilingualism– that we hope will be useful in this regard. We conclude with new questions to nudge our discipline towards a more nuanced, inclusive, and socially-informed scientific understanding of multilingual experience. We hope to engage a wide array of researchers united under the broad umbrella of multilingualism (e.g., researchers in neurocognition, sociolinguistics, and applied scientists).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".