Review Article What is GIS and What is Not?
Bibliographic record
Abstract
As the title implies, this review of GIS (both GI Systems and GI Science) talks about boundaries – what is “in ” and what is “out”. In order to do this, the discussion itself must have some boundaries. A Canadian prime minister once described his country as “too much geography and not enough history”. Here we will try to minimize both the history and the geography, and see how GI Science (the discipline) impinges on a variety of other disciplines, from Astronomy to Zoology perhaps. In other words, the boundaries of GI Science are becoming much less clear-cut, and fuzzier. It is important not merely to look at the traditional “Geo- ” disciplines, but at other subjects which can contribute by overlapping with GI Science. In many cases this overlap may be more a question of mutually useful technology than similarity of applications – so even the distinction between GI Systems (the technology) and GI Science is a fuzzy one. This review is just one opinion, and a brief one at that, of where GIS fits at one moment in time. It will inevitably conflict in parts with the opinions of others. In outline we will look briefly at the initial situation, and see
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".