Antiracist Interventive Interviewing: Subverting Colonial Interventions with Public Sector Workers
Bibliographic record
Abstract
What does it mean to intervene in antiracist interviews with public sector workers? What do interventions look like in research seeking to name complicity in settler colonial violence and imagine otherwise relationships between non-Indigenous and Indigenous people? How might we methodologically define interventions and their pedagogical purpose(s)? In this paper, we share our experience of adopting a dual-pedagogical antiracist interventive research methodology in our qualitative research with public sector workers on settler colonial socialization. Building on antiracist interventive interviewing method, we map out our conceptualization of interventions as multidirectional and multiscalar. We narrate how we see interventions as dual pedagogical moments of disruption and possibility occurring at three scales, where we intervene to support our participants’ learning and they intervene to support ours. Our approach is illuminated through illustrations from our transcribed data of virtual interviews with 32 public sector workers in BC ( n = 23) and Alberta ( n = 9), and through our reflections on our research process. Our analysis demonstrates that interventions have three key effects. First, they are generatively disruptive in that they offer better access to understanding processes of settler colonial socialization. Second, interventions create junctures for antiracist and anticolonial learning. Third, interventions with participants open up opportunities to imagine otherwise beyond the strictures of settler colonialism, and orient towards anticolonial praxis rooted in recognition of Indigenous sovereignties. We conclude with a vocabulary of interventions meant to offer other qualitative researchers possibilities for how to intervene to better access and disrupt sites of deep colonizing.
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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 | 0.046 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".