Replication of scientific research: addressing geoprivacy, confidentiality, and data sharing challenges in geospatial research
Bibliographic record
Abstract
The ability to replicate, or reproduce, research is fundamental to the scientific process. Research combining a variety of georeferenced data is spreading rapidly across scientific domains and international borders. This suggests a growing potential for the use and integration of new and existing data sets to create new multi-disciplinary scientific collaborations. Yet, the unique characteristics of georeferenced data present special challenges to such collaborations. These data are highly identifiable when presented in maps and other visualizations or when combined with sensor data or other related geospatial data sets. The potential opportunities of collaboration may thus be constrained by the need to protect the locational privacy (geoprivacy) and confidentiality of subjects in research using georeferenced data. This paper reviews the obstacles to and potential methods for sharing georeferenced data in order to support a growing and dynamic geospatial research community and build capacity for data-intensive research across the social and environmental sciences. The development and implementation of a geospatial virtual data enclave methodology is proposed as an innovative and viable solution to share and archive georeferenced data among researchers while protecting the geoprivacy of research subjects and the confidentiality of these data. The ability to share confidential geospatial data among researchers is crucial to ensuring replicability of scientific research, and to enable researchers to verify and build upon the research of others.
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 | 0.080 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.018 | 0.038 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".