On-line Reporting and Mapping of Spatially Aggregated Individual Records Selected by User Queries
Bibliographic record
Abstract
In this article, an on-line system allowing users to perform detailed queries of a database of individual records containing confidential information is described. The results of the query can be reported in table, comma-delimited file, or map format for spatial aggregates defined by the user. This system addresses some important social problems arising from the development of large digital databases of information on individuals that can be analysed using GIS, including violating privacy and confidentiality protections, profiling, and distributing multiple, unregulated copies of source databases. It also recognizes the needs of public agencies to distribute information and of individuals to have access to public information. The system is designed as an open-architecture server-side system. A database of six years' worth of injury mortality records compiled from data provided by the Connecticut Department of Public Health is developed to demonstrate the process of formulating user queries and the corresponding results. The prototype system described uses technology to address the needs of public agencies to provide access to public information in a way that ensures database integrity, protects privacy and confidentiality, and enhances access to public information.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".