Geography and geographic information science: An evolving relationship
Bibliographic record
Abstract
GISystems have strong and longstanding roots in Geography, stemming from early developments in the 1960s and 1970s that defined a first phase of their relationship. But as the uses and sophistication of geospatial technology have grown and spread across virtually all areas of the academy, reducing Geography's claim to ownership, that relationship to Geography has evolved in new directions, forming a second phase. The critiques of the early 1990s have led to research into the societal context and social implications of GISystems that remains largely centred in Geography; techniques for the analysis of data embedded in space and time remain strongly associated with Geography; and rigorous principles have been discovered under the umbrella of GIScience that are widely recognized within and outside Geography. Today the relationship has entered a third phase, defined by the new opportunities that are being created by the growth of data science, by new sensors, and by new areas of application, suggesting that the relationship between Geography and GIScience will continue to evolve in interesting and exciting ways.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.026 | 0.043 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".