Integrating human geography into futures studies: Reconstructing and reimagining the future of space
Bibliographic record
Abstract
Abstract This article explores the concept of the “future” through the lens of human geography. We examine how space may influence the way we perceive the future and how actors connected to this space will determine or undermine the kind of future to be unfolded. Particularly, we are interested in who influences the ideas of the future that explains how futures could be imagined and constructed. Already, the ideas of the future have utilized concepts of sustainability and climate change to demonstrate how futures may unfold. However, these envisioned futures, which mostly originate from a narrow perspective within a single space–time dimension, can be misleading. The ideas of the future can be challenged because space–time evolution alters the social structure of actors connected to space in multiple dimensions. As space–time evolves, new actors will be introduced, and actors who have been traditionally power‐less may emerge to contest and negotiate access to power to provide alternative ideas of the future. Understanding how power is negotiated and contested by these actors in the future is critical to understanding who has the future power.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".