My Junglee Story Matters: Autoethnography and Language Planning and Policy
Bibliographic record
Abstract
The present paper discuses the value of autoethnography as a research methodology in the area of language planning and policy in investigations of language, power, and identity. Traditionally, research methodology in the area of language planning and policy focuses on language, power, and identity from a sociopolitical perspective at the national level. These methodologies do not easily examine how the issues of language, power, and identity are related to the lives of individuals. Therefore, this paper argues for the use of autoethnography as a research methodology in language planning and policy research because it systematically analyzes personal experiences in order to understand the researcher’s cultural experience regarding her or his perspectives, beliefs, and practices of language as a language user. This paper also argues that autoethnography can be combined with traditional research methods such as historical-structural analysis and ethnography of language policy to make language planning and policy research more diverse and critical.Keywords: Language planning and policy; autoethnography; research methodology; power; identity
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".