From Rural China to the Digital Wilds: Negotiating Digital Repertoires to Claim the Right to Speak
Bibliographic record
Abstract
Abstract Based on data from a qualitative case study of two Chinese university EFL learners from rural backgrounds, Andy and Jimmy, this study traces their progress from being struggling English language learners to confident speakers of English. Drawing on Darvin and Norton's (2015) model of investment that recognizes the intersection of identity, capital, and ideology, this study dissects Andy and Jimmy's engagement with the digital wilds and their negotiation of resources in online and offline environments. Analysis of interviews, observations, and digital artifacts reveal that as students from rural and migrant worker families, Andy and Jimmy positioned themselves and were positioned by others as inadequate speakers of English, contributing to their initial non‐participation in the English classroom. Participation in the digital wilds however provided these learners with opportunities to acquire a wider range of resources and to reframe their identities as legitimate speakers. Such expanded repertoires empowered them to claim the right to speak and to be heard across online and offline spaces. These findings reiterate the pedagogical potential of the digital wilds in creating conditions that enable rural EFL learners to invest in their learning of English.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".