National Achievement Award, Canadian Linguistic Association Prix national d'excellence, Association canadienne de linguistique 2021
Bibliographic record
Abstract
Learnability and L2 Phonology was the first to adapt models of language learnability to the question of how second-language learners acquire phonological knowledge, taking seriously the notion of L2 phonology as cognition.His approach laid the groundwork for much future research on interlanguage grammars and the architecture of the bilingual mind, and is regularly covered in textbooks in the field.In his current work, he is probing recursion and representational realism through research on second language acquisition at the interfaces of morphology and syntax.In addition to his own research, Dr. Archibald has served the field as a reviewer, adjudicator, and editor.Dr. Archibald is also an extremely effective bridge-builder, bringing together the worlds of theoretical linguistics and L2 pedagogy.He has influenced thousands through his work on the benefits of second language education and bilingualism, including the production of the video Advantage for Life: Learning Another
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.204 | 0.057 |
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".