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Record W7079970008 · doi:10.7275/livinglanguages.2018

“AUTHENTIC” L2 REVITALIZATION IN KANIEN’KÉHA: THE CASE OF IDIOMS

2025· article· en· W7079970008 on OpenAlexaffabout

Bibliographic record

VenueUniversity of Massachusetts (UMass) Amherst · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProsodyIndigenousFocus (optics)Identity (music)Neuroscience of multilingualismLanguage proficiencyFirst language

Abstract

fetched live from OpenAlex

Kanien’kéha (Mohawk) is an Iroquoian language with fewer than 700 speakers in six communities across Ontario and Quebec (DeCaire 2023). Similarly to most Indigenous languages in Canada, the residential school system led to an abrupt break in its inter-generational transmission in the mid-20th century. Since the 1970s, Kanien’kehá:ka communities have insisted upon the importance of preserving their language, seen as crucial for their identity and culture. This has led to the development of many revitalization projects, the most significant of which are adult immersion programs (Maracle 2002). These stand out by following the strategy of L2 revitalization: create new L2 speakers of child-bearing age, and have them raise L1 children in the language, thereby restoring inter-generational transmission. As articulated by Kanien’kéha teachers themselves, this approach is highly successful, with the key caveat that, to ensure the language survives with minimal influence from English, these L2 speakers must acquire and transmit an “authentic” form of the language (i.e. emulating L1 speech) (Green and Maracle 2018). However, achieving “authenticity” (Hinton and Ahlers 1999) can be challenging, and immersion graduates frequently lack a final layer of proficiency (e.g. L1-like prosody and discourse), hindering communication with L1s. This work investigates this difficulty of acquiring “authentic” Kanien’kéha through a specific case study: idioms (fixed non-compositional expressions, e.g. wa’kerihwahní:rate’ “I confirmed it”, literally “I-idea-hardened-it”). More precisely, I tackle the following issue: what are the implications of idioms for the restoration of Kanien’kéha inter-generational transmission through “authentic” L2 revitalization? To this end, I conducted focus groups with five expert Kanien’kéha language workers, and made the research process as collaborative as possible by letting participants lead the discussion. They arrived at the following conclusions: idioms are crucial for humour and expressive ability, and are thus a central component of the “authentic” Kanien’kéha that must be preserved; but they are semantically opaque and difficult to use in the appropriate contexts for L2 learners, something which can only be remedied by extensive post-immersion exposure to L1 speech. This work contributes to both language revitalization as an academic discipline, as the implications of idioms for revitalization remain under-studied, and the revitalization of Kanien’kéha itself, by clarifying the challenges involved in this issue and suggesting concrete solutions to them. I also hope that this project may constitute another example of the value and feasibility of mobilizing scholarship to serve the interests of collaborating Indigenous communities, following Cameron et al.’s (1992) Empowerment Model.

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)0.007
metaresearch head score (Gemma)0.007
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 score0.960
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0330.027
Scholarly communication0.0120.012
Open science0.0020.010
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0080.001

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 head0.009
GPT teacher head0.201
Teacher spread0.193 · 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
Published2025
Admission routes2
Has abstractyes

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