Prix d'excellence étudiante Student Achievement Awards ACL / CLA 2023 York University
Bibliographic record
Abstract
Kanien'kéha noun incorporation: A dichotomous reanalysis" Martin Renard a présenté une nouvelle analyse d'un phénomène déjà familier : l'incorporation de noms en kanien'kéha, une langue iroquoienne parlée dans certaines parties du Canada et des États-Unis.Martin a utilisé des données recueillies dans le cadre de son propre travail de terrain pour comparer les approches lexicales et syntaxiques de l'incorporation des noms.Les membres de l'auditoire ont été impressionnés par les connaissances approfondies de Martin en matière de littérature et par sa capacité à utiliser de nouvelles données et analyses pour aborder un vieux débat.La communication s'est démarquée par son style clair et convaincant, l'accessibilité des documents pertinents et l'assurance avec laquelle Martin a géré la période de questions.*** Martin Renard's talk presented a novel analysis of a well-studied phenomenon: noun incorporation in Kanien'kéha, an Iroquoian language spoken in parts of Canada and the United States.Martin used data collected in his own fieldwork to compare lexical and syntactic approaches to noun incorporation.Attendees were impressed with Martin's comprehensive knowledge of the literature and his skill in using new data and analyses to tackle an old debate.The presentation was notable for its clear and compelling style, the accessibility of the relevant materials, and Martin's confident handling of the question period. Meilleure affiche / Best posterKaitlyn Owens (Indiana University, Bloomington): "Re-evaluating the role of duration in Laurentian French high vowel laxing" L'affiche de Kaitlyn Owens présente une analyse phonétique du relâchement vocalique dans un volumineux corpus de français laurentien (québécois).Elle a trouvé que le relâchement vocalique ne peut pas être prédit par la durée seule et que,
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.245 | 0.112 |
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".