Bibliographic record
Abstract
In this paper, I argue that what makes geography stand out among other academic disciplines is not its collection of methods, but instead the collection of key geographical concepts that are encountered with high frequency in its corpus of published scholarship. I illustrate how this way of thinking works in practice by taking as a case study the emergent field of the geographies of the future and suggesting that it is the very same set of key geographical concepts that makes this field stand out from the more amorphous realm of “futures studies.” I begin my analysis by providing a brief literature review of the seven main research clusters within the field of the geographies of the future: (1) risk, uncertainty, contingency, and surprise; (2) neoliberal governmentality and its management of the future; (3) prefigurative politics and visions of a postcapitalist future; (4) technological progress as a key dimension to foreseeing the future; (5) the future in light of social difference; (6) culture and the historicizing of the future; and (7) economic geographies of the future. Then, in the final part of the paper, I offer some suggestions on how the careful and creative deployment of these key geographical concepts can deepen and enrich the way we think about the future and its geographies. Specifically, I organize these suggestions into three analytical clusters, focusing on (1) distance and proximity; (2) scale; and (3) borders and territory. I then provide some final thoughts about the key concepts versus key methods controversy, arguing in favor of the former.
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 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.082 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".