Bibliographic record
Abstract
Critical GIScience encompasses a range of geographic information systems (GIS) research that focuses not so much on algorithmic or computational advances, but rather on the social implications of and social biases inherent in the science, technology, and their deployments. First posited as a separate strand of research in 1999, it slowly evolved into a recognized component of GIScience with its own themes, researchers, and advocates. Theoretically, critical GIScience should be integrated into mainstream implementation; in practice, it exists as a separate stream of investigation – owing to historical precedents as well as the difficulty of integrating social theoretical insights into technological practice. As Web 2.0 and big data evolve and affect the discipline of Geography, a new critical GIScience is morphing into an umbrella term that encompasses many aspects of change to GIS and mapping, including location‐based services, volunteered geographic information, big data, and potential loss of privacy.
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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.247 | 0.106 |
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".