Significant Advances in Applied Geography from Combining Curiosity-Driven and Client-Driven Research Methodologies
Bibliographic record
Abstract
The central thesis of the 2005 Anderson Lecture is that significant achievements in applied geography occur when the principles and practices of curiosity-driven and client-driven research are combined in the statement of problem, the idealized and operational research design, and the procedures of evaluating results. A companion thesis extends the Anderson Lectures by Jack Dangermond, Brian Berry, and Tom Wilbanks by positing that the best of applied geography incorporates a commutative perspective when establishing the parameters of an inquiry. That is, using pair wise combinations for illustration, research study parameters such as epistemology-praxis, conceptual-empirical, spatial-aspatial, theory-hypothesis, method-technique, causeeffect, analysis-synthesis, and structure-function are necessary elements in applied research that validates geography as a science-based, societally-relevant discipline, and geographers as professional practitioners. The examples of remote sensing, optimization techniques, decision support systems, geographic information systems, and the Walking Security Index project are used to illustrate how significant advances in applied geography result from combining curiosity-driven and client-driven research methodologies.
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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 | 0.104 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".