Constructing Roma students as ethnic ‘others’ through orientalist discourses in Bulgarian schools
Bibliographic record
Abstract
This article uses the case of Bulgarian, predominantly Roma, schools to illustrate the long history of stereotypes about Roma people dating back to modernity’s discursive binary oppositions of ‘civilized’ vs. ‘barbarians.’ The data from a longitudinal study with 12 Bulgarian educators showed the modes by which Roma as the Other is created in the school context as a universal cognitive category, internalized in social and individual identities that divide the world into ‘us’ and ‘them.’ The paper argues that Bulgarian teachers’ perceptions of attitudes, behavior, and values of Roma communities are, in fact, a projection of the discursive representations with which western European modernity has constructed the Balkan region. This research contributes to further explicating how the ideological paradigm of neoliberalism intersects with the old Enlightenment and post-Enlightenment dichotomy of civilized–barbarians and how it is reconfigured to construct those incapable of fitting within the entrepreneurial spirit of the free market efficiency as unwilling to democratize. The case of Bulgarian, predominantly Roma, schools serves to illustrate how peoples who are Othered in the western European discourse designate their own Other, and thus provides a fruitful approach to understanding how Roma’s social exclusion is constructed and situated.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.059 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".