Bibliographic record
Abstract
In this third report, I focus on cognitive cartography in order to examine how the historical division between empiricist and critical approaches in cartography has shifted recently. I do so by building on Kitchin and Dodge’s argument (2007) that parts of the apparent disjuncture within cartography might be resolved through a greater focus on emergent approaches to mapping as a process, which is the core idea of post-representational cartography. By looking at cognitive cartography from a post-representational perspective I emphasize two major trends. On the one hand, the processual positioning of post-representational cartography simply shifts the historical line of divide, since it inherently disqualifies any cognitive studies that artificially dissociate the map from its context of use and production. On the other hand, by enabling the combination of critical positioning with empiricist practices, post-representational cartography offers opportunities to revisit in practical terms the tensions between these two approaches. It provides an original framework to envision our mental, emotional and embodied relationships with maps and with places through maps, and has the potential to bring cartography into a new arena in which the empiricist/critical divide could be transcended.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.013 |
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".