“Ongoing harm under a prettier, brighter umbrella”: A critical analysis of reconciliation talk in Canadian settler philanthropy
Bibliographic record
Abstract
Abstract Engagement with the concept of reconciliation, broadly understood as the process or goal of transforming relations among Canada's Indigenous and non‐Indigenous peoples, became commonplace in the philanthropic sector after the 2015 release of the final report of the Truth and Reconciliation Commission of Canada (TRC). Institutional responses to the TRC (e.g., by universities, businesses, healthcare institutions, governments, etc.) have been widely discussed in the academic literature—lauded by some as pivotal to the transformation of Indigenous/settler relations, and critiqued by many as no more than a shift in rhetoric covering over ongoing racialized colonial violence. Through a critical analysis of reconciliation talk as articulated through blog posts, press articles, conference recordings, webinars, and professional development resources produced by and for progressive settler philanthropy from 2012 to 2020, this essay analyses the hearty uptake of reconciliation in the sector. I bring these texts into dialog with the larger body of critiques of reconciliation, with the aim of interrogating settler philanthropy's place and roles in Canada's settler colonial order, past, and present. I focus on common terms and concepts that appear in conjunction with reconciliation talk such as diversity and inclusion; community‐engagement and relationship building; learning and listening. I also explore instances of obfuscation and renaming, such as a scarcity of explicit references to race and racism, colonialism and white supremacy and a tendency to relegate colonialism to the past in texts produced by non‐Indigenous authors. The themes I identify across these texts resemble those apparent in other institutional contexts. Reconciliation talk has potential to transform how settler philanthropy engages with Indigenous communities. Yet, many of the most urgent critiques raised in other fields apply to this context. Ultimately, I conclude, reconciliation talk may elevate and uphold—and indeed conceal—the white supremacist, colonial status quo in settler philanthropy and in Canada more broadly. Occlusions and renaming common to settler philanthropy's reconciliation talk contribute to what Vimalassery et al. describe as “colonial unknowing” and what Tuck and Yang call “settler moves to innocence.” In these ways, reconciliation serves a mystifying function for settler philanthropy, masking ongoing coloniality, absolving settler guilt, and avoiding more radical, transformative possibilities.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".