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Record W3008632098 · doi:10.18806/tesl.v36i3.1325

Social Media for Social Inclusion: Barriers to Participation in Target-Language Online Communities

2019· article· en· W3008632098 on OpenAlexvenueno aff
Ellen Yeh, Nicholas Swinehart

Bibliographic record

VenueTESL Canada Journal · 2019
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)Social mediaSociologyComputer-mediated communicationPsychologyPedagogyHumanitiesLinguisticsThe InternetWorld Wide WebComputer scienceGender studiesArt

Abstract

fetched live from OpenAlex

Many learners, even those studying at universities in regions where the target language is spoken, lack opportunities for meaningful language use outside of the classroom. One avenue for learners to increase authentic target-language communication is online affinity spaces within social media platforms, where interactions with other users are formed around shared interests rather than personal connections. International students at an arts and media college in the Midwestern United States were asked to read a discussion thread within a social media platform, summarize what they found useful, and respond to pre- and posttask questionnaires. The platform used, Reddit, features anonymous user-generated content in a wide range of discussion forums based around specific interests and geographic locations. This study used qualitative data to investigate the extent to which international students participate in online communities like these and the factors or barriers that keep them from achieving full participation. The findings are then used to present learner training strategies that can help reduce or remove those barriers, enabling language learners to increase their participation in target-language online communities. Plusieurs apprenantes et apprenants, même parmi celles et ceux qui étudient dans une université située dans une région où la langue cible est parlée, n’ont pas suffisamment d’occasions de pratiquer avantageusement leur nouvelle langue en dehors de la salle de classe. Une avenue qui s’ouvre à elles et à eux pour augmenter leurs chances de s’adonner à des communications authentiques dans leur langue cible est l’espace d’affinité en ligne sur les réseaux sociaux, endroit où les interactions sont davantage basées sur le partage d’intérêts communs que sur des relations personnelles. Des étudiantes et étudiants internationaux d’un collège des arts et des médias du Midwest des États-Unis ont été invités à lire un fil de discussion sur une plateforme de réseau social, à en résumer les éléments jugés utiles et à répondre à un questionnaire avant et après l’exercice. La plateforme utilisée, Reddit, présente des contenus qui sont générés anonymement par les utilisateurs dans un large éventail de forums de discussion et qui sont regroupés autour d’intérêts et de secteurs géographiques particuliers. Cette étude utilise des données qualitatives permettant d’évaluer dans quelle mesure les étudiantes et étudiants internationaux participent à la vie de communautés en ligne de ce genre et de déterminer quels sont les facteurs ou obstacles qui les empêchent de le faire pleinement. Les constatations sont ensuite utilisées pour présenter des stratégies de formation des apprenantes et apprenants qui sont susceptibles d’aider à réduire ou aplanir ces obstacles et à aider par le fait même les participants à s’impliquer davantage dans la vie de communautés en ligne s’exprimant dans leur langue cible.

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)0.011
metaresearch head score (Gemma)0.046
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 score0.014
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.006
Scholarly communication0.0110.009
Open science0.0020.016
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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