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Record W2118706135 · doi:10.5539/ijel.v1n2p281

Investigating Language-Related Episodes during Mechanical and Meaningful Output Activities

2011· article· en· W2118706135 on OpenAlexvenueno aff
Shirin Abadikhah

Bibliographic record

VenueInternational Journal of English Linguistics · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)GrammarLexisSet (abstract data type)LinguisticsPsychologyOutcome (game theory)Focus (optics)Principal (computer security)Computer scienceNatural language processingMathematicsPhysicsPhilosophy

Abstract

fetched live from OpenAlex

The present study examines how EFL learners consciously reflect on their language during a set of mechanical and meaningful output activities. Thirty-six Farsi learners of English negotiated on linguistic features and completed six activities over a period of six weeks. The transcripts from the learners’ interaction were analyzed for instances of language-related episodes (LREs), their principal focus on meaning or grammar and their nature and outcome. The results showed that (1) the meaningful output activities elicited significantly more LREs than did the mechanical output activities, (2) while approximately half of the LREs in the meaningful activities focused on lexis and meaning, the majority of LREs in the mechanical activities were directed towards grammatical forms and a small portion was focused on meaning, (3) the two output groups differed significantly in the continuous and correctly solved episodes. The study provides support on the effectiveness of collaborative output activities in pushing learners to verbalize their internal linguistic processing and focusing their attention on a wide range of linguistic features.

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)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.030
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.256
Teacher spread0.223 · 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 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

Citations10
Published2011
Admission routes1
Has abstractyes

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