Growing a Radical Food Geographies Praxis
Bibliographic record
Abstract
As a concept and practice, radical food geographies (RFG) praxis brings together attention to power, knowledge, and resistance to the inequities that shape food systems around the world in diverse ways. RFG draws key elements from radical geographies and critical food systems scholarship towards making food systems more just and sustainable. Critical to the RFG approach is a praxis that pushes food systems scholars and activists to pursue work that is simultaneously grounded in theorizing, action, and reflection related to the intersections of place and spatiality. Praxis refers to the dynamic interconnections between theory and practice, critical reflection, and the resulting actions (for example, see Freire, 1970; Gramsci, 1971; hooks, 1994). We conceive of RFG praxis as having three interconnected elements: (1) theoretical engagement with power and structures of oppression both inside and outside the academy; (2) action through academic, social movement, and civil society collaborations; and (3) analysis through a broadly defined geographic lens (see Figure 1.1). In this chapter, we provide an overview of the scholarly foundations of RFG praxis. Radical geographies foundations Radical geographies have integrated and built upon different geographical contexts, theories, and activities worldwide. They identify, explain, and impact relations of power, oppression, and capitalist exploitation in particular places and across space. In North America, what has been formally referred to as radical geography (a subdiscipline in the academic field of geography) traces its roots to a rejection of the US status quo in the 1960s (contemporaneous with the US Civil Rights Movement, the Black Power Movement, the Chicano Movement, the American Indian Movement, second- wave feminism, and opposition to the Vietnam War). The group of self- described ‘radical geographers’, grounded in Marxist and anarchist thinking, sought a restructuring of society. They were also challenging their discipline, one that was rooted in positivist and quantitative approaches that they insisted were unresponsive to societal crises and reproduced oppressive structures (Fuller and Kitchin, 2004). The overarching position of these primarily anglophone and minority world- based individuals was that the field of geography needed to account for its complicity and contributions to the accumulation of power and capital.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".