The Natures of Maps: Cartographic Constructions of the Natural World
Bibliographic record
Abstract
Editor's Note We are pleased to introduce a new section in Cartographica devoted to a series of invited critiques and commentary on a target article. For the inaugural contribution, we have chosen to examine chapter 1 of a new book by Denis Wood and John Fels, The Natures of Maps (University of Chicago Press, 2008). Responses to this piece have been provided by Chris Perkins (University of Manchester, UK), Gwilym Eades (McGill University, Montreal, Canada), and Rob Kitchin (National University of Ireland, Maynooth). Wood and Fels then offer a short reply. Note that, for reasons of space and of clarity, some notes have been modified in the version provided here, and the colour figures that appear in the book have been omitted. Except in quoted material, US spellings have been replaced by Canadian spellings. (Jeremy W. Crampton)
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".