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Record W4235751090 · doi:10.22215/etd/2020-14125

The Role of Documentary Linguistics in the Creation of Academic Resources and Materials for Second Language Learners: Conversations with Kanien’kéha Students and Teachers

2020· dissertation· en· W4235751090 on OpenAlexaff
Caitlin Bergin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsCarleton University
Fundersnot available
KeywordsDocumentationFluencyRelevance (law)LinguisticsPerceptionPsychologyPedagogyComputer scienceMathematics educationPolitical science

Abstract

fetched live from OpenAlex

This thesis aims to contribute to an emerging literature in documentary linguistics (Woodbury, 2003;Himmelman, 1998Himmelman, , 2006;;Amery, 2009) which examines the relevance and application of permanent language resources to contexts of second language education.Semi-structured interviews were conducted with adult language learners and teachers at Onkwawén:na Kentyóhkwa, an adult immersion school in Ohswé:ken, to gather perceptions and ideas on how and what types of data collected in linguistic documentation projects could be utilized to create language learning materials.Kanien'kéha students and teachers were also asked to comment on their perceptions of or experiences with documentary linguists and how these relationships could be improved.Interviews were transcribed and coded thematically to identify emergent themes revealing contributor perspectives.Findings illustrate that, although Kanien'kéha students and teachers have access to some educational materials and resources, contributors made specific requests for documentation, including motherese, idiomatic expressions and every day, interactional speech.Contributors also provided information on how the field of language documentation and practices could be improved.These suggestions included improving accessibility to documentary data, learning the language which is under study, and maintaining communication with the participants and communities.The contributors also made suggestions for ways in which linguists could help Indigenous language education programs succeed.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.013
Scholarly communication0.0160.011
Open science0.0010.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.024
GPT teacher head0.434
Teacher spread0.409 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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