Bibliographic record
Abstract
Transportation is the major factor contributing to climate change. We decided to address the issue of transportation in relation to climate change for this research-informed action (RiA) project. The purpose of our RiA project was to learn about transportation and climate change, conduct a mini-correlational study, and address the issue by encouraging students and their parents to limit car use. After learning that a larger portion of the boys and girls that we surveyed rely on cars to get to school we decided to organize an event called “No Car Day” at Erindale SS on March 22, 2013. This coincided with Earth Hour. In addition, we made announcements from Monday to Thursday to inform student s about this specific event. We counted the number of cars prior to the event and found that an average of 156 cars dropped students off in the morning. On the morning of the event, we counted the number of cars again. This time, the number of cars was 115.We think that our campaign made a difference. We want to encourage students in other schools to organize similar events. We encourage our peers and our teachers to carpool, take the bus, bike or walk. If we all do our part, then we may be able to alleviate the negative effects that transportation has on climate change before it is too late.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.013 |
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".