Illegally Blonde: The Racialisation of Blondness and Visual Representations of Palestinian Activist Ahed Tamimi in American and Canadian Media
Bibliographic record
Abstract
Informed by theories of media representation, Orientalism, and settler colonialism, this research endeavours to contribute to the discussion on the impact of media representation within a specific political context. It intends to reveal the power dimensions and ideological positions embedded in dominant media discourses in North America. Five news videos, three from Canadian, and two from American online daily media sources, are selected carefully during December 2017 and July 2018 when the Israeli army arrested Ahed Tamimi. In terms of methodologies, adopts Chouliaraki's (2011) multimodality model to analyse the visual and semiotic choices made by the news editors and draws on Fairclough's (1995) conception of Critical Discourse Analysis (CDA) for its contextual analysis. The findings suggest that through different discursive and representational strategies, the media frame Tamimi and the Palestinians as violent initiators. Moreover, Tamimi's blondness and her ‘Western’ look are marked as ‘fake’ and ‘propaganda’, thus establishing the new norm of representing ‘Otherness’. These strategies echo accepted values in American and Canadian societies and their foreign policies in the past decade. The results also achieve the purpose of legitimising the use of state violence on colonised bodies, which ultimately reflects settler-colonial history in North America.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".