Enlisting Students to Transcribe Historical Climate and Weather Data For Research: Building Knowledge Translation Via Classroom-Based Citizen Science
Bibliographic record
Abstract
DRAW (Data Rescue: Archives & Weather) is a citizen science project that asks the Canadian public to take part in transcribing millions of meteorological observations recorded between 1871 and 1963 at McGill University’s Observatory in Montreal, Quebec, which was demolished in 1963. We examine how classroom-based curricula can integrate citizen science so youth can learn more about their community via engagement with the local history of weather conditions and impacts. Conducted in March 2018, this research examined knowledge translation during a three-week course module through written reflections, classroom video footage, exit interviews, and a final group research assignment. We worked with 21 students—16- to 20-year-olds enrolled in a social science research methods course at Dawson College, a two-year collège d'enseignement général et professionnel (college of general and vocational education) that attracts local students and is a funded part of education in the province of Quebec. We found knowledge translation was facilitated by student engagement with their community’s history and appreciation for aiding credible scientific research. Knowledge translation suffered from attempts to include archival records that could be difficult to find, access, and read. Our work showed that citizen science, as a vehicle for community engagement and scientific literacy, requires considerable contextualization, for example, the use of frequently asked questions, tutorials, and blogs for context, and historical context to ensure knowledge translation takes place.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".