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Record W4414387377 · doi:10.1111/tops.70026

Material Anchors in Language Learning

2025· article· en· W4414387377 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTopics in Cognitive Science · 2025
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCognitionIndigenousCognitive linguisticsIndigenous languageChart

Abstract

fetched live from OpenAlex

Language-learning materials are cognitive technologies that aim to facilitate the complex cognitive task of acquiring the means for effective communication in a second language. Blackfoot and Plains Cree are two closely related Indigenous languages spoken in Canada and the United States. Both languages are now rarely learned by children, but both have growing language teaching traditions. As polysynthetic agglutinating languages, Blackfoot and Plains Cree pose a challenge for speakers of English, due to the typological distance between Germanic and Algonquian languages. Developing language-learning materials to overcome this challenge constitutes an important step in ensuring language sustainability. Cognitive Anthropology and Cognitive Linguistics offer theoretical frameworks for the study of language-learning materials as cognitive technologies. This study examines the Plains Cree Syllabary chart as a cognitive tool for acquiring a new writing system. On this basis, a new chart is developed that supports the learning of Blackfoot grammatical structure.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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.

Opus teacher head0.019
GPT teacher head0.392
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it