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Record W4239746845 · doi:10.3138/cmlr.62.1.137

Pedagogy, Purpose, and the Second Language Learner in On-Line Communities

2005· article· en· W4239746845 on OpenAlexvenueno aff
Diane Potts

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDialogicReciprocity (cultural anthropology)SociologySpace (punctuation)PedagogyExperiential learningCultural capitalPsychologyMathematics educationLinguisticsSocial science

Abstract

fetched live from OpenAlex

Information and communication technologies (ICTs) are often portrayed as offering a collaborative community space in which native and non-native language speakers reciprocally scaffold linguistic, cultural and content knowledge, a space which assists students in overcoming well-documented challenges encountered in traditional classrooms (Leki, 2001; Morita, 2000, 2004). However, recent studies point to the communicative disjunctures arising from variances in cultural capital and socio-technological histories in on-line dialogic encounters between student groups (see Belz, 2003; Kramsch & Thorne, 2002; Thorne, 2003, 2000). This article examines online community formation among participants in a graduate seminar on modern language education and the pedagogical design that facilitated the development of norms of joint construction of knowledge, reciprocity, and sharing. Drawing upon survey and interview data as well as on a descriptive statistical analysis of the bulletin board interaction, the study explores how design provided non-native speakers with opportunities to capitalize on their existing experiential and intellectual capital.

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)0.011
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 score0.012
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.010
Scholarly communication0.0120.005
Open science0.0010.008
Research integrity0.0010.002
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 head0.028
GPT teacher head0.262
Teacher spread0.234 · 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

Citations29
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Modern Language Review/ La Revue canadienne des langues vivantesSame topicEFL/ESL Teaching and LearningFrench-language works237,207