Rethinking Multilingual Experience through a Systems Framework of Bilingualism: Response to Commentaries
Bibliographic record
Abstract
Abstract In Rethinking Multilingual Experience through a Systems Framework of Bilingualism (Titone & Tiv, 2022), we encouraged psycholinguists and cognitive neuroscientists to consider integrating social and ecological aspects of multilingualism into a collective understanding of its cognitive and neurocognitive bases (i.e., to rethink experience). We then offered a framework – the Systems Framework of Bilingualism– and described empirical challenges and potential solutions with applying this framework to new research. Since the paper's publication, several eminent colleagues read and commented on our Keynote, noting both its strengths and areas for improvement. We read each commentary with enthusiasm and gratitude. Here, we briefly respond to several salient points raised, which led us to clarify and improve our theoretical approach. We first address what the commentaries agreed were strengths of the framework. We follow this with a discussion of what the commentaries stated could be improved or extended. We conclude with ways that we modified our model to collectively address concerns raised in the commentaries.
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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 | 0.043 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.025 | 0.061 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".