Searching for social justice in GIScience publications
Bibliographic record
Abstract
Maps are explicitly positioned within the realms of power, representation, and epistemology; this article sets out to explore how these ideas are manifest in the academic Geographic Information Science (GIScience) literature. We analyze 10 years of literature (2005–2014) from top tier GIScience journals specific to the geoweb and geographic crowdsourcing. We then broaden our search to include three additional journals outside the technical GIScience journals and contrast them to the initial findings. We use this comparison to discuss the apparent technical and social divide present within the literature. Our findings demonstrate little explicit engagement with topics of social justice, marginalization, and empowerment within our subset of almost 1200 GIScience papers. The social, environmental, and political nature of participation, mapmaking, and maps necessitates greater reflection on the creation, design, and implementation of the geoweb and geographic crowdsourcing. We argue that the merging of the technical and social has already occurred in practice, and for GIScience to remain relevant for contributors and users of crowdsourced maps, researchers and practitioners must heed two decades of calls for substantial and critical engagement with the geoweb and crowdsourcing as social, environmental, and political processes.
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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 | 0.052 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.048 | 0.074 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.033 | 0.031 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".