Bibliographic record
Abstract
This commentary addresses three issues that arise in the context of linguistic distance and crosslinguistic differences, namely how linguistic distance is defined, how linguistic distance translates into linguistic knowledge, and what the relationship is between linguistic distance and crosslinguistic influence. As far as distance is concerned, articles in this issue differ as to whether they adopt external or internal measures of language distance, raising the question of how externally defined language relatedness translates into the internalized grammar of an individual learner. As for crosslinguistic differences, there is an assumption in some of the articles that the more different/typologically apart the languages are, the harder the second language (L2) will be to acquire and the greater the prospect of first language (L1) transfer. In contrast, several articles show that typological closeness does not necessarily facilitate acquisition, while distance does not impede it. Discrepancies and commonalities between the various approaches are discussed.
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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 | 0.016 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.043 | 0.035 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".