NEW GROUNDS FOR INTERLINGUISTIC ANALYSIS OF THE ACQUISITION OF ROMANCE AND SLAVIC LANGUAGES AS FOREIGN BY ENGLISH, FRENCH, AND UKRAINIAN NATIVE SPEAKERS
Bibliographic record
Abstract
Learning a new language is a difficult task. It requires skills for memorizing new words, learning how to put words together in a grammatical way, and integrating them with existing linguistic knowledge. In 2016 researchers at the Donders Institute and Max Plank Institute for Psycholinguistics observed these skills through brain imaging as native speakers of Dutch learned another language and discovered that the brain cares whether or not the grammatical properties of the new language resemble the grammar properties of the native language. If they are similar, the brain uses its own in learning the new language [5]. Does this statement bring us back to the 13th century when the idea of Universal Grammar was first formed by Roger Bacon in his Overview of Grammar [6]? We can answer with definitely “Yes”. This idea passed through the centuries and in the 1950s was reemerged for the umpteenth time by an American professor and public intellectual known for his work in linguistics, political activism, and social criticism Naom Chomsky [1].
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".