‘Can I speak? If I can, I speak what you want me to speak’: Negligence to the Adivasi Language in the Bangladeshi Education System
Bibliographic record
Abstract
This paper presents a frame analysis of the state and education policy and Bangladeshi practices regarding its negligence towards the Adivasi (indigenous) languages in the education system for the Adivasi students through the lens of Linguicism and Critical Race Theory (CRT). This analysis argues that the policy of equal and same education for everybody titled ‘education for all in Bangla’ promotes the overarching sentiment of Bangali nationalism rooted in Bangla language, and endorses inequitable though racist practices for the Adivasi people in Bangladesh. Also, I demonstrate that the attitude of ‘disavowal’ for the Adivasi languages as well as knowledge feeds on existing identity politics regarding ‘indigeneity’ and the assimilation process. Finally, while portraying the dominant attitude of the power structure (political/social Elite) of misrecognition and disregard for the existence of these Adivasi people as well as their languages within the education system, this analysis shows how education, a colonial legacy has been used as a technology of power to propagate ongoing colonialism, oppression and discrimination in Bangladesh. Therefore, this article attempts to delineate how such discriminatory educational policies and practices towards the Adivasi reflect the underlying ideologies of centralization, homogenization, standardization, hierarchization and colonization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".