Geographical Sensemaking: Situating, Relating, and Positioning as Spatial Practices Between Self and World
Bibliographic record
Abstract
How people attach meaning to space is one of the most central questions of human geography. Space has no meaning in itself but must be made meaningful through human action. The aim of this article is to take a closer look at this process of geographical sensemaking. Although most human geographers would agree that space is bound to certain subjective relations to become meaningful, which spatial practices geographical sensemaking is based on still remains an open question. Drawing on an international research project involving image-based interviews in Singapore, Vancouver, and Berlin, we lay out three such practices that contribute to geographical sensemaking: situating, relating, and positioning. We outline an empirically grounded framework that aims to grasp how people make sense of spaces they see: how they contextualize and familiarize themselves with spaces previously unknown to them, how they draw links between these spaces and their own biographies, and how they position themselves and develop a political stance in relation to these spaces. In conclusion, this study highlights the importance of investigating geographical sensemaking for understanding the shaping of differing self–world relationships.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".