Bibliographic record
Abstract
With disciplinary roots in imperialism and the military, geography has long engaged with security issues. As the French geographer Yves Lacoste (1976) famously stated, “La géographie ça sert d'abord à faire la guerre” (Geography serves, first and foremost, to wage war). Mainstream geography grounded in materialist positivism provides spatial information to identify and address a broad range of security risks, including crime and terrorism but also natural disasters, diseases, pollutants, or chronic poverty. In contrast, critical geography drawing from anarchism, feminism, and postmodernism mostly seeks to demonstrate how insecurity is often paradoxically generated by dominant security discourses and practices, while striving to bring about progressive alternatives. Geography is thus not only a discipline deploying spatial analysis to achieve greater security, but also one concerned with the consequences of “securitization” and with emancipatory possibilities for a less vulnerable world. This chapter provides a survey of geography's engagement with concepts of security, charting some of the main questions, theoretical approaches, and methodologies of this broad discipline before discussing some of its strengths and limitations. Geography has been and remains deeply connected with “official” security agendas (Mamadouh 2004; O'Loughlin and Heske 1991). Tasked with the mission of “knowing the world” and helping to pinpoint the location and movements of threats, geography and geographers have been mobilized in the production of military maps, atlases, and systems of geosurveillance ranging from CCTV to drones and satellites. Indeed, many professional geographers have served, and continue to serve, “national security” agendas – some in the direct employ of the military (Woodward 2005). My own alma mater department at Oxford was the first academic home of Professor Halford Mackinder (of “geographical pivot of history” fame); during the Second World War, the department also played an active role in the British war effort, assembling an odd mix of spatial information – from geological surveys to tourism leaflets – to produce British operational maps. The department, anecdotally, was also said to be a place for MI6 to recruit undergraduates. Geography, from this perspective, is partially a discipline in the service of statecraft – a necessary instrument of spatial analysis in the toolbox of security practices, as seen in the context of 9/11 and the “War on Terror” (Cutter et al . 2003; Flint 2003). Yet geography is also a discipline engaging more broadly with “security.”
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".