Bibliographic record
Abstract
"Critical geographers" concerned with cartography insist maps are first and foremost social artefacts that must be "read" as authorial documents rather than perceived as data statements. Their argument is typically dismissed as trivial because the relation between intent and output has rarely been demonstrated in a critical way. This article seeks to demonstrate the degree to which authorial intent defines map content and appearance through an analysis of a single set of maps. All are based on the original data collected in 1854 by Dr John Snow as part of his study of the cholera outbreak in London's Soho district. Snow's original map is included as baseline for a study that includes versions of the Snow map by a range of authors, including Cliff and Haggett; the US Centers for Disease Control; Gilbert; Tufte; and Monmonier. The resulting appropriations bear progressively less resemblance to the original work, despite the use of the same data set and its clear availability. The result is a cautionary tale of the distance between maps and the data they represent. The article also insists upon the close relation between authorial intent and mapped result irrespective of the data available. Finally, the article concludes that because mapping is not value-free, dangers occur when professional cartographers and geographers attempt to map data from fields in which they are ignorant.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".