Bibliographic record
Abstract
For the past three decades Denis Wood has explored the nature and power of maps; how maps are designed, used, and understood, the role of maps in society; and cartographic theory more broadly. His collaboration with John Fels, The Natures of Maps, furthers this project and seeks to detail both the nature of maps and the nature of maps. For Wood and Fels, ontological thinking about cartography has been fixated on the nature of maps. They illustrate this argument with reference to Arthur Robinson and J.B. Harley, two cartographic theorists with very different ideas about the ontology of maps – maps as objective truths and maps as social constructions. Wood and Fels argue that, despite their differences, Robinson and Harley both conceive of a map as having an inherent truth (they note that for Harley the map itself remains ideologically neutral, with ideology bound to the subject of the map and not the map itself). Wood and Fels reject this position to argue that the map itself, its very make-up and construction – its selfpresentation and design, its symbol set and categorization, its attendant text and supporting discourse – is ideologically loaded to convey a particular message. In so doing, a map does not simply represent the world, it produces the world. To illustrate their argument, they use the example of the nature of a map – how the supposedly neutral, objective natural world is produced by maps – to demonstrate how maps produce nature rather than reflect it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".