Autoethnography of an English language teacher in a postcolonial context- coming to terms with my shifting positions, my ‘lack’ and finding my ‘becoming’ in relation to my “social capitals”.
Bibliographic record
Abstract
As a lecturer of English at an elite English medium private university in Bangladesh, I often was troubled by the thought that I did not have the elite English like my colleagues who had attended English medium schools. Among the three mediums of schooling system existent in Bangladesh, I went to a Bengali medium school. However, the medium of my schooling was never a concern for me until I had joined this university and interacted with people who spoke an elite version of English. The thought of not attending an English medium school affected me so much that I subconsciously tried to become like my colleagues from English medium schools and tried to hide my Bengali medium schooling background. This autoethnography looks into why and how I wanted to hide a core part of my life- my schooling background, and how I embraced the same schooling background later. This story uncovers the power dynamics of two languages-English and Bengali in postcolonial Bangladesh and in myself, and how the superior status of English affected my being. The story also reveals my understanding of ‘becoming’ which will hopefully benefit my future research work on second language learners’ identity development.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".