Utilization of Google enterprise tools to georeference survey data among hard-to-reach groups: strategic application in international settings
Bibliographic record
Abstract
BACKGROUND: As geospatial data have become increasingly integral to health and human rights research, their collection using formal address designations or paper maps has been complicated by numerous factors, including poor cartographic literacy, nomenclature imprecision, and human error. As part of a longitudinal study of people who inject drugs in Tijuana, Mexico, respondents were prompted to georeference specific experiences. RESULTS: At baseline, only about one third of the 737 participants were native to Tijuana, underscoring prevalence of migration/deportation experience. Areas frequented typically represented locations with no street address (e.g. informal encampments). Through web-based cartographic technology and participatory mapping, this study was able to overcome the use of vernacular names and difficulties mapping liminal spaces in generating georeferenced data points that were subsequently analyzed in other research. CONCLUSION: Integrating low-threshold virtual navigation as part of data collection can enhance investigations of mobile populations, informal settlements, and other locations in research into structural production of health at low- or no cost. However, further research into user experience is warranted.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".