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Record W2748274906 · doi:10.5539/elt.v10n9p161

Agency Construction and Navigation in Oral Narratives of English Learning by Chinese College English Majors

2017· article· en· W2748274906 on OpenAlexvenueno aff
Qiuming Lin

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)Construct (python library)NarrativeDilemmaLinguisticsTransitive relationSystemic functional linguisticsPsychologyIdentity (music)SociologyEpistemologyComputer scienceSocial scienceAesthetics

Abstract

fetched live from OpenAlex

The current study aims to investigate the discursive construction and navigation of agency in oral narratives of English learning by Chinese college English majors. Based on the theoretical framework integrating Bamberg et. al.’s theory of identity dilemma and Hallidayan systemic functional linguistics, the study has addressed two research questions: 1) How do the speakers construct different levels of agency in their narratives? 2) How do the speakers navigate among different levels of agency throughout their narratives? The research data comes from monthly-based individual interviews with the participants for one year, from which significant English-learning stories are selected. Then transitivity analysis and logico-semantic analysis are conducted to the stories clause by clause so as to find out the linguistic patterns for agency construction and navigation. The study has found that speakers construct different levels of agency with various transitivity patterns, and navigate the agency dilemma by moving back and forth among different levels of agency with various logico-semantic relations. It has also illustrated that agency is not a fixed entity that a speaker possesses, but constructed and negotiated dynamically by the speaker all the way through his/her narratives.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.111
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.267
Teacher spread0.259 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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