A Moment of Reckoning: \n \nReconciliation Through Decolonial Prefiguration in a Food Movement Organization
Bibliographic record
Abstract
There is an increasing recognition that settler colonialism is a root cause of food insecurity for Indigenous Peoples, and that it is also a contributor to the food insecurity of Black people and people of colour. Recent research reveals stark racial disparities, with food insecurity 4.3 times higher in Indigenous households and 2.6 times higher in Black households compared with white households (First Nations Food, Nutrition and Environment Study, 2019; Statistics Canada, 2017). Food movements are a forum through which multiple groups seek to address the lived experience of inequity. However, as predominantly white/settler-led, food movement organizations fail to adequately address the unequal impacts of food injustice and may even be complicit in perpetuating colonial and racist structures and processes. In this research, I examine a specific “moment of reckoning” at Food Secure Canada’s 2018 Assembly, arguably the largest food movement event in Canada, and its aftermath through the analysis of 124 qualitative questionnaires, ten interviews and participant observation. Using two foundational treaties as a conceptual framework, this case study demonstrates how by refusing settler processes and structures to make space for resurgence, Indigenous Peoples, Black people and people of colour are creating the conditions needed for reconciliation as transformation, rather than assimilation. This study also shows the importance of white/settlers responding by taking on the work of personal (un)learning and making concrete organizational change to governance and procedures in order to enact their distinct responsibilities to decolonize in order to reconcile with Indigenous Peoples, Black people and people of colour. The lessons learned apply widely across community organizations, advocacy groups and social movement spaces as well as public and private institutions working towards reconciliation and decolonization.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.039 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".