Unraveling the local tapestry: exploring English language learning motivations in Taiwan’s unique cultural landscape
Bibliographic record
Abstract
Abstract This study delves into the intricacies of English language learning motivations in Taiwan’s unique cultural landscape by unraveling the local tapestry of sociocultural factors and individual perceptions. Utilizing a structured photo-elicitation method across a range of ages and socioeconomic conditions, results revealed four main emergent categories. Sense of self represents informants’ struggle with English use in social settings, where the language is rare in daily life. Facilitating conditions reflects the tension arising from English requirements contrasted with limited practical applications. Worldliness represents how informants actively situate English in their daily lives, primarily through media. Lastly, perceived goals concerns social mobility and travel abroad, aligning with expectations of gaining competitiveness in the neoliberal global economy. This study highlights the importance of examining motivation in English language learning within a context of local influences while considering the transmodality of local language practices. Our findings suggest that English language learning motivation in Taiwan is shaped by strong motivators that do not fit neatly into an extrinsic/intrinsic dichotomy. This research contributes to the understanding of language learning motivation, emphasizing the need to unravel the local tapestry of factors that shape individual motivations and frame English language learning experiences.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".