Human Security and Food Security in Geographical Study: Pragmatic Concepts or Elusive Theory?
Bibliographic record
Abstract
Abstract This article explores how the concepts of ‘food security’ and ‘human security’ build common language between action‐based researchers and policy‐makers for effective knowledge translation. Both concepts are important examples of how research can inform policy aimed at social justice. While human security is still building a sense of itself, the concept of food security demonstrates how broader issues can be excluded from the common language and how this limits dialogue. In the process of building common language between researchers and policy‐makers, the agreed definition of food security excluded many important issues. As a result, the excluded, more radical issues have been pursued by off‐shoot movements that do less to directly engage the policy‐making process. Human security is a similar concept that is at risk of abandoning its radical origins in order to be considered workable by policy‐makers and academics alike. I argue that social justice theorization must maintain the audacity to envision radical improvements to the human condition, albeit pursuing working definitions for policy‐makers. Social justice and action‐based research should not shy away from theory that seeks to overcome inequity and injustice; it should work to meet the needs of people rather than meet the needs of existing policy‐making technocracy.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.145 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".