The Possibilities of Geopoetics for Growing Radical Food Geographies and Rooting Responsibilities on Indigenous Lands
Bibliographic record
Abstract
We are four researchers with past and present ties to the Health Arts Research Centre located within the territory of the Lheidli T’enneh First Nation, in the city now known as Prince George in the region of northern British Columbia (BC), Canada. As a collective, our research delves into the health inequities between Indigenous Peoples and settlers in so- called Canada, inequities that are rooted in systemic racism and upheld by colonial governance systems through policies, laws, and everyday practices (Allan and Smylie, 2015). Tracing the implications of these inequities for sovereign Indigenous foodways, and with our hearts and minds seeking redress, we take up Morrison's (2011: 98) call to ‘[respect] the sovereign rights and power of each distinct nation’ to practise Indigenous food sovereignty. In this chapter, we reflect on the complex enactment of settler solidarity with Indigenous Peoples and nations that stems from honouring ancestral land rights and title, and respecting the jurisdiction of Indigenous legal systems, systems that are foundationally life- affirming (McGregor, 2009), and which protect and sustain sovereign foodways (Daigle, 2016, 2019b). We discuss the opportunities that such solidarity, forged through an ethics of settler responsibility (Phung, 2019), might contribute to growing radical food geographies (RFG) praxis (Hammelman et al, 2020; see also Levkoe et al, Chapter 1, this volume). Seeking to build solidarity through an ethics of settler responsibility is well aligned with the three interconnected elements of RFG praxis advanced by the editors of this book. First, our ‘theoretical engagement with power and structures of oppression’ focuses on the ongoing coloniality perpetuated by the Canadian state in sanctioning contested industrial resource extraction projects on Indigenous lands and criminalizing Indigenous land defenders who assert their sovereign rights in opposition. We call on settler scholars and activists to educate ourselves about Indigenous rights and title, and to respect and uphold the governance of Indigenous legal systems. Second, by discussing the example of settler– Indigenous solidarity at the Senden Sustainable Agriculture Resource Centre detailed throughout this chapter, we reiterate the role of civil society – and especially grassroots – actors in grounding academic work on food systems and driving social change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.058 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".