Unicorns, Leprechauns, and White Allies: Exploring the Space Between Intent and Action
Bibliographic record
Abstract
Being a White ally goes beyond being merely “non-racist” and having good intentions. Meaningful allyship is behavioural and requires active participation in dismantling systems of oppression. The objective of this study was to ascertain the degree to which White individuals behave in an allied manner when provided the opportunity to do so by comparing observed racial justice allyship behaviour to self-reported allyship behaviours. Using a subsample (N=31) from a larger study, White participants took part in a laboratory behavioural task where they engaged in three 5-minute discussions with another White participant (a confederate) about racially-charged news stories in the United States while knowingly being watched by a Black RA via live recording. Stories represented different forms of racism towards Black people: the removal of a Confederate monument; the killing of an unarmed Black male college student by police after a car accident; and a fraternity party where members dressed up as Black stereotypes. Coders were asked to rate how they believed a Person of Colour would feel interacting with that participant using a 4-point Likert scale: 0 (absence of any supportive comments) to 3 (very explicit, unwavering support for non-racist and equity values and behaviour). Furthermore, a newly developed self-report questionnaire indicating interpersonal allyship (IRAS) was used to ascertain self-reported allyship. Results showed that when using a mean cut-off score of 2 as an indicator of allyship for each laboratory scenario (consistent support throughout the interaction), only 6.4% of participants met these criteria. Furthermore, only 3.2% of the participants were allies in all 3 scenarios, 9.7% were allies in 2 scenarios, and 16.1% were allies in 1 scenario. The results indicated that White people consistently showed a lack of allyship towards Black people. We discuss the challenges of allyship, and the difference between White allies and White saviors. Future research should expand on and explore the complexities and nuances of meaningful White allyship.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".