Introduction: Issues of Privacy Protection and Analysis of Public Health Data
Bibliographic record
Abstract
GIS has witnessed tremendous development in the last decade or so. As the reach of GIS technologies and applications expanded, its effect is also increasingly felt by a variety of individuals and social groups far beyond the realm of the research community. Critics of GIS have identified various social consequences associated with the use and development of GIS, while GIS scientists and researchers have responded to these criticisms through new initiatives that address various social problems arising from the use of GIS data, algorithms, software, and hardware. The purpose of this theme issue is to show that certain types of social problems associated with the use of GIS may be addressed in the technical realm. Its four main articles focus mainly on issues of privacy protection and analysis of public health data. The commentary article highlights the critical issues that emerge from the main articles. With a focus on privacy protection and methods for analysing health data, these articles provide good examples of how the social implications of GIS may be addressed in the technical realm.
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".