Bibliographic record
Abstract
Geographic information systems (GIS) technology is increasingly being used by nongovernmental organizations, grassroots organizations and other activist groups involved in transforming social, economic and environmental policy in multiple countries. The use of GIS represents a response to the fact that environmental problems are multidimensional and refuse to acknowledge political borders. It also represents a growing awareness that, for activism to compete in an era of globalization, it must utilize tools that scale from a local to a multinational level . A research field called public participation GIS (PPGIS) has emerged to investigate the use and value of GIS by marginalized peoples and communities engaged in social change. It has yet to formally examine cross‐border and multinational applications. This paper makes a substantial contribution to moving the PPGIS research agenda forward to pace existing nonprofit activities. The paper considers the critical aspects of PPGIS being used across borders and in scaling up nonprofit organizations. The paper briefly reviews the PPGIS literature on issues of resources and data access and the role of GIS expertise. It then analyzes the use of PPGIS across borders as a function of building organizational capacity. Theory is reinforced with examples of nonprofits currently using GIS in multiple countries. A transnational PPGIS is framed, which can serve as a base for further investigation and discussion .
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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".