Citizen Science and Community Engagement in Tick Surveillance—A Canadian Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lyme disease is the most common tick-borne disease in North America and Europe, and on-going surveillance is required to monitor the spread of the tick vectors as their populations expand under the influence of climate change. Active surveillance involves teams of researchers collecting ticks from field locations with the potential to be sites of establishing tick populations. This process is labor- and time-intensive, limiting the number of sites monitored and the frequency of monitoring. Citizen science initiatives are ideally suited to address this logistical problem and generate high-density and complex data from sites of community importance. In 2014, the same region was monitored by academic researchers, public health workers, and citizen scientists, allowing a comparison of the strengths and weaknesses of each type of surveillance effort. Four community members persisted with tick collections over several years, collectively recovering several hundred ticks. Although deviations from standard surveillance protocols and the choice of tick surveillance sites makes the incorporation of community-generated data into conventional surveillance analyses more complex, this citizen science data remains useful in providing high-density longitudinal tick surveillance of a small area in which detailed ecological observations can be made. Most importantly, partnership between community members and researchers has proven a powerful tool in educating communities about of the risk of tick-vectored diseases and in encouraging tick bite prevention.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it