Cultures of Enthusiasm: An Ethnographic Study of Amateur Map-Maker Communities
Bibliographic record
Abstract
Further attention should be paid to contextualizing the spaces and practices of amateur map-making. Doing so will provide further insight into the ways that maps, mapping epistemologies, and mapper identities emerge in the practices of everyday life. In order to deconstruct the map, and ultimately the power of maps, it is necessary to investigate maps from the bottom up as well as the top down. The motivations of amateur cartographers, the contexts in which map-making takes place, and the technological processes involved are all important factors to consider when examining how and why maps are produced. Empirical ethnographic evidence from a study of OpenStreetMap and humanitarian “mapping parties” is presented here to demonstrate how the often overlooked cultures of amateur map-making offer novel perspectives on who contemporary map-makers are and what motivates them to map. It is shown that amateur map-making is a broad category that includes close-knit hobby communities and more diverse groups of enthusiastic volunteers. As cartography continues to open up and become more accessible through a range of digital mapping technologies, studying these shifts will be important for understanding how and why the role of the map in contemporary life is changing.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".