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Record W2575093256 · doi:10.5539/ijel.v7n2p124

Retrospects and Introspects of Researches on Listening Strategies at Home and Abroad

2017· article· en· W2575093256 on OpenAlexvenueno aff
Huajie Zhou, Wang Zhi

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningStatus quoPsychologyInformational listeningProcess (computing)Field (mathematics)CognitionCognitive strategyMathematics educationListening comprehensionComputer sciencePolitical scienceCommunication

Abstract

fetched live from OpenAlex

Listening strategies refer to thoughts or activities that language learners often use in the process of listening. Listening strategies are the vital branches of learning strategies, and as researches of learning strategies become a specialized research field, researches of listening strategies have spread to the aspects of meta-cognitive strategies, cognitive strategies, socialized and affective strategies, strategy-training, strategy-guiding, strategy-instruction etc. In recent years, with the continuous efforts of scholars at home and abroad, researches of listening strategies tend to be more elaborate in depth and length, which reflects on research contents, research fields, research methods, research subjects etc. In this paper, the authors have mainly made researches on the research status quo of English listening strategies at home and abroad and have given reflections on the research deficiencies. It is expected that we could improve both the learners’ listening proficiency and the strategy-instruction in EFL classrooms at home and abroad.

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)0.008
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0090.005
Science and technology studies0.0030.004
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.375
Teacher spread0.351 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicSecond Language Acquisition and LearningFrench-language works237,207