Bibliographic record
Abstract
The role of language teacher agency in language policy and planning (LPP) enactment and implementation at the micro-level has received increasing treatment in the literature. Under-addressed in this context, however, is the role of the learner and the extent to which learner activity can be agentive. Seeking to redress this situation, this paper focusses on learner agency in LPP. After establishing a general ecology of language context, issues related to the problematic concept of ‘agency’ are addressed. This discussion draws upon poststructuralist critiques as well as the insights of sociocultural theory. A poststructuralist perspective provides a broad philosophical base for problematizing learner agency and supplies a critique of the limited structuralist approach characteristic of traditional LPP. A sociocultural lens supplies a more concrete conceptualization of how agentive learner activity operates interactively with teacher agency. The final section of the paper focusses on ethnography as a research methodology; ethnographic research yields qualitative data on learner agency that can be drawn upon in micro planning and policy-making. A relevant case study employing ethnographic methodology is discussed. The conclusion is that learner agency should be given more prominence in LPP research and literature.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".