Engaging Students in Atmospheric Science: A University-High School Collaboration in British Columbia, Canada
Bibliographic record
Abstract
Five high schools in British Columbia, Canada, participated in an atmospheric sciences project during the winter of 2006-07 established by researchers at the University of Calgary. Precipitation gauges and temperature and relative humidity probes were installed at each school and students were asked to collect a water sample each day that precipitation accumulated. These samples were used to trace the evolution of stable water isotopes across southwestern Canada. Researchers visited schools to talk about water resources and climate change, and data were collated and given to teachers to use in an atmospheric science project. The participatory nature of this project gave students exposure to data collection and basic analytical techniques used in atmospheric sciences. This was a first attempt at collaboration between our research group and secondary schools, and we point out a number of issues that arose in our study with respect to a successful two-way engagement between researchers and students. These include school engagement, the geographic distribution of the participating schools, the time span of the project, and the time available to schools. There are also a number of data quality considerations, but we were successful overall in acquiring a unique, high-quality dataset that satisfies our research objectives.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.063 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".