Bibliographic record
Abstract
We do not always think of the impact we are making on the climate every time we turn on a light, use a computer or watch T.V. For this research-informed action research project we studied how electricity usage can impact the climate. We conducted a mini correlational study at Erindale Secondary School to learn more about our peers’ electricity consumption. We asked our peers how many fluorescent light bulbs they use in their homes, and how often they turn off their lights when they leave the room. Our mini study reveals that the majority of boys and girls do not know how many light bulbs in their homes are energy efficient. In addition, equal proportion of girls and boys always or sometimes turn off the lights when they are not in use. For the action portion of our project, we organized the Earth Hour at our school to lower the energy consumption and to raise awareness about the importance of energy conservation. The Principal of our school also agreed to turn off the lights in the cafeteria every night to save more energy. We also challenged David Suzuki Secondary School in Mississauga to a friendly competition to see which school will save more energy. We still await the electricity usage data from the board to see who won the challenge.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".