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Record W4404313289 · doi:10.18806/tesl.v41i1/1400

Parlure Games

2024· article· en· W4404313289 on OpenAlexaffvenueabout
Rhonda Chung, Walcir Cardoso

Bibliographic record

VenueTESL Canada Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsConcordia University
Fundersnot available
KeywordsLinguisticsPsychologySociologyPedagogyMathematics educationPhilosophy

Abstract

fetched live from OpenAlex

Reterritorialization is an imperial process that creates settler colonial nations, like Canada, and funds intergenerational settler policies to assert intergenerational control over unceded territory, like English-only and French-only teacher education programs. This results in pedagogies designed to discourage learners from exploring other languages, instead focusing on the learning of low-variable, standardized materials (e.g., from mass media), which privilege social speech markers indexed to white native speakers. Such invariability is neither sociocognitively advantageous to learners nor linked to robust language learning, predicting miscommunication. To address this lack of variation in the imperial language curriculum, we developed Parlure Games, a computer-assisted language-learning tool that promotes exposure to and interaction with highly variable audiovisual social speech markers (via high-variability phonetic training: HVPT, a technique that enhances learning through varied input), while also scaffolding land-sensitizing activities critical of imperial sprawl using online mapping. In this paper, we report on the development of Parlure Games, explore its pedagogical affordances, and assess its acceptance as a de/colonizing audiovisual learning tool by teacher candidates enrolled in a TESL program in Quebec. By providing opportunities to interact with diverse social speech markers, Parlure Games provides a means to pluralize the imperial classroom while sensitizing instructors to its reterritorializing processes. La reterritorialisation est un processus impérial qui crée des états coloniaux, comme le Canada, et qui finance des politiques intergénérationnelles de colonisation afin d’exercer un contrôle intergénérationnel sur des territoires non cédés, comme les programmes de formation des enseignants de l’anglais ou du français uniquement. Il en résulte des pédagogies linguistiques conçues pour décourager les apprenants d’explorer d’autres langues, se concentrant plutôt sur l’apprentissage de matériels standardisés à faible variabilité (par exemple, du contenu des médias de masse) qui privilégient les marqueurs de discours sociaux associés aux locuteurs natifs de race blanche. Cette invariabilité n’est ni sociocognitivement avantageuse pour les apprenants ni liée à un apprentissage solide de la langue, prédisant plutôt des erreurs de communication. Pour remédier à ce manque de variation dans les programmes d’enseignement des langues impériales, nous avons développé Parlure Games, un outil d’apprentissage des langues assisté par ordinateur qui favorise le contact et l’interaction avec des marqueurs de discours sociaux et audiovisuels très variables (à travers l’entraînement phonétique de haute variabilité : une technique qui promeut l’apprentissage à l’aide d’un intrant diversifié), tout en favorisant des activités de sensibilisation aux territoires qui critiquent l’étalement impérial à l’aide de la cartographie en ligne. Dans cet article, nous rendons compte du développement de Parlure Games, explorons son potentiel pédagogique et évaluons son acceptation en tant qu’outil d’apprentissage audiovisuel décolonisant par de futurs enseignants inscrits à un programme d’enseignement de l’anglais langue seconde au Québec. En offrant la possibilité d’interagir avec divers marqueurs de discours sociaux, Parlure Games fournit un moyen pour diversifier la classe impériale tout en sensibilisant les enseignants à ses processus de reterritorialisation.

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 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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0450.000

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 head0.045
GPT teacher head0.449
Teacher spread0.403 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations2
Published2024
Admission routes3
Has abstractyes

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