Research Data in Google Earth: How Do We Protect Privacy and Meet Ethical Obligations?
Bibliographic record
Abstract
In 2005, Google released Google Earth (a free virtual globe, map and geographic information program) allowing anyone with location coordinates (postal codes, GPS data) to create and share accurate spatial-location maps. It has since become a powerful tool for researchers and scientists in the growing discipline of visual data analysis. In 2008, Fleet and Williamson demonstrated analytic and communication benefits by mapping the survey data of 400 small and medium businesses in Atlantic Canada. Yet, our (research) ethical obligation is to ensure the confidentiality of identification information received from respondents, and the postal code data entered into geo-analytic tools such as Google Earth allow the viewer to see identification pins marking individual buildings or rooftops. This paper will summarize the Canadian requirements on privacy, confidentiality and identification issues and requirements for Canadian researchers (as defined by the Canadian Tri-Council Policy Statement). It will conclude with a series of proposals and discussion points (both technical and non-technical) for how to address these concerns.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".