Beyond ‘Good Intentions’: Fostering Meaningful Indigenous–Settler Relationships to Support Indigenous Food Sovereignty
Bibliographic record
Abstract
Indigenous Peoples in (so-called) Canada face deep and long-standing injustices. Today, many face significantly higher than average rates of food insecurity, lack access to safe drinking water and health services, and have limited control over their traditional territories for cultural food provisioning, thereby limiting capacity for self-determination. At the same time, Indigenous communities have continued to assert their sovereignty and resist settler colonialism. Civil society organizations (CSOs) and academics have sought to establish partnership-based projects that intend to work toward addressing many of these inequities. Despite ‘good intentions’, many of these initiatives have failed to substantially benefit Indigenous people. Barriers to creating meaningful partnerships include power relations that keep decision-making in the hands of settlers, competing values and priorities, conflict over jurisdictional issues, and assumptions inherent in current funding models. Radical food geographies (RFG) praxis offers a promising framework to explore these issues because of its recognition of the relational production of injustice in specific contexts, its valuing of diverse ways of knowing, and its multiscalar approach. The RFG framework guides our collaborative writing project to co-create insights about establishing meaningful partnerships among Indigenous and settler Peoples to advance Indigenous food sovereignty. The authors draw on our collective experiences within four Indigenous-led and Indigenous-serving CSOs that have been working for many years in partnership with communities to support Indigenous food sovereignty. We share stories and learnings from our work with an aim to advance RFG by exploring how organizations and academics supporting Indigenous food sovereignty can create partnerships that operate in ethical space with Indigenous Peoples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".