Searching for social justice in GIScience publications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it