Perspectives on an Evolving Research Field: Location Intelligence and Its Representation at the Applied Geography Conferences, 1978 to 2012
Bibliographic record
Abstract
Geography has an established relevance to many of the most important challenges facing society across the human–environmental spectrum. Among many areas of application, geography has a historical record of connecting location concepts, tools, and expertise to the key planning and operational issues confronting business and other institutions in society. This article examines the context for applied geographic research falling within this location intelligence sphere, and profiles the body of research in this field published at the Applied Geography Conferences over its first thirty-five years. Our analysis shows that although location intelligence has had an ongoing representation at the conference, its presence has fluctuated greatly. The disciplinary profile developed here tracks the shifting emphasis of location intelligence research and its relation to broader, real-world needs. We conclude by interpreting these findings and making recommendations related to increased self-assessment and repositioning of research in the location intelligence community.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".