‘Justin Trudeau has had a colourful few days, hasn’t he?’: Blackface and Politics
Bibliographic record
Abstract
Individuals use discursive techniques to justify racism and avoid sounding racist in everyday life. The main aim of this research was to examine one specific instance of potential racism, involving Canadian Prime Minster Justin Trudeau’s use of ‘blackface’. Techniques of discourse analysis were used to analyse media coverage of Trudeau’s previous actions in adopting ‘blackface’, following his public apologies for these actions. Four thematic strands of argument were identified in an analysis of the articles gathered from online media sources surrounding the incident. The forms of argument used by the media to criticise Trudeau and his actions were as follows: (1) describing blacking-up as racist; (2) presenting defence of this behaviour as politically motivated; (3) questioning all Trudeau’s behaviour, and (4) depicting Trudeau as a flawed character. These arguments function both as a focus for media criticisms of Trudeau and his actions and as a basis for producing further criticisms of his policies and political actions. They do at the same time point to some of the issues involved in determining what is to count as racism in current society. Further research is needed to explore more fully the extent to which such argumentative strands are deployed more widely in political debate.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.045 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".