Children’s accounts of labelling and stigmatization in private schools in Delhi, India and the Right to Education Act
Bibliographic record
Abstract
India’s Right of Children to Free and Compulsory Education Act, 2009 compels private schools to reserve a proportion of their seats for free for disadvantaged children. Although controversial, it is idealized as an equity measure for inclusion in and through education. This small-scale study, feeding into a larger research project, details children’s accounts of their everyday lived experiences at private schools in Delhi. Children reported labelling students by teachers as ‘naughty’ or academically ‘weak’ or ‘incapable’ as a pervasive practice. These ‘designated identities’ (Sfard & Prusak, 2005) were reinforced by teachers and through peer interactions. They were internalized by participants about their peers and affected how they interacted with them. Peers who were labelled were reported to be stigmatized. Surprisingly, neither caste nor gender were mentioned as explicitly marking participant experiences. The paper also discusses the participatory methods employed in the study as a further contribution to the literature on private schooling. Data are from participatory ‘draw-and-talk’ sessions conducted with 16 children in 2015-16 from marginalized backgrounds, accessing six different private schools in one catchment area, half of whom secured a free private school seat. Participants were from amongst the first cohorts eligible for the free seats provision.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".