The Roma and Wall Street/CEOs: linguistic construction of identity in U.S. and Canadian crime reports
Bibliographic record
Abstract
Discriminatory practices against Roma (also known as Romanies) occur on a daily basis in many countries around the world through media discourse. This paper investigates the representation of Romanies in U.S. and Canadian online newspaper crime reports and compares this representation to Wall Street/CEOs in crime reports demonstrating how identity of both groups is constructed through a variety of linguistic and non-linguistic strategies. Drawing on Mayr and Machin’s (2012) critical linguistic analysis of the language of crime, this multimodal study incorporates a variety of tools such as Critical Discourse Analysis and Cognitive Linguistics in order to dig below the surface to reveal ideological frames. Results illustrate the denaturalization (and negative representation) of Romanies and contrasting naturalization of CEOs and point to a growing need for consciousness-raising through critical linguistic analysis such as this in order to continue to fight for social change and a more just system for the Roma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".