Beyond absolute space: modeling disease dispersion and reactive actions from a multi-spatialization perspective
Bibliographic record
Abstract
Dynamic geographical phenomena, such as the transmission of communicable diseases, are inherently complex processes. Concerns have arisen in the GIScience community that the prevailing absolute spatialization is insufficient to capture the complexity. This study investigates health risks in the frame of multiple spatializations: relational space (home and workplaces), relative space (serviceplaces), and mental space (perception). First, we estimate health risks in terms of the presence of influenza-like illness symptoms in relational space and relative space. Second, we estimate the probability of taking reactive actions based on health threats perceived in mental space. A two-layer Bayesian network model and the SHAP model are used to support the intended study. Findings reaffirm the pivotal role of relational space in disease transmission. Relative space is found to impose substantial health risks that exceeded those of relational space, yet the risks varied across serviceplace types. Perceived health threats in mental space effectively motivated reactive actions. The multi-spatialization frame enables the representation of health risks at the nexus of proximity, relations, relative contacts, and perception, and can be extended to many geographical phenomena both theoretically and empirically.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".