Construction of a Space–Scene–Scenario (3S) research framework in human geography in the AI era and its interdisciplinary applications
Bibliographic record
Abstract
In the era of artificial intelligence (AI), numerous geographical issues require interdisciplinary approaches and theories. As the demand for cross-disciplinary problem-solving increases, human geography, which addresses challenges from a spatial perspective, faces methodological challenges in maintaining its distinctiveness. Scholars often approach urban, economic, and sociocultural issues within human geography through the lens of its subfields, resulting in a gradual decline in the recognition of human geography as an integrated discipline. This paper seeks to review the development and research methodologies of human geography systematically and, in response to the needs of interdisciplinary research, proposes the Space–Scene–Scenario (3S) research framework. This framework integrates dimensional and scalar thinking approaches within human geography, offering a comprehensive pathway—from identifying research starting points to uncovering research objectives—to analyze societal problems from a human geography perspective. Moreover, it is designed to adapt to the societal transformations and technological impacts brought about by AI. The flexibility and inclusiveness of the 3S framework not only enhance the distinctiveness of human geography as a discipline but also promote a holistic and integrative research approach that thereby transcends traditional subfield boundaries. Additionally, through the integration of theory and practice, this paper demonstrates how the 3S framework functions as a versatile tool for investigating spatial inequalities. Its ultimate aim is to guide human geographers in enriching the theoretical system of human geography while affirming their discipline’s value in the context of the AI era.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".