Bibliographic record
Abstract
The global food system is a major contributor to climate change, producing 19 - 29 per cent of greenhouse gas emissions (Vermeulen, et al., 2012). This system is entirely controlled by humans, and therefore, we are responsible for the negative effects of this system on the well-being of our planet. The large amount of greenhouse gas emissions occurs mainly due to food miles — the distance that food is transported from producer to consumer — and our meat consumption. In this research-informed action report I conducted a study to: i) determine if there was correlation between gender and meat consumption; and ii) if there was a correlation between gender and purchasing of locally grown foods. I learned that 67 per cent of high school boys and 71 per cent of girls that I surveyed consumed meat more than 4 times a week. Based on this data, I concluded that there was no correlation between gender and meat consumption amongst teenagers that I surveyed. However, I felt that meat consumption was rather high. Also, about 40 percent of boys and girls sometimes buy locally grown food, which is great, but a third of them did not know if the food they, or their parent, purchase is locally grown. Once again, I concluded that there was no correlation between gender and habits of purchasing locally grown foods. In repose to this, I decided to take an action and develop a video to make my peers more aware of global food systems and their relationship to climate change. I posted the video on FaceBookTM where my family and friends can view it and comment. I hope that this project inspires everyone to be more conscious about their diets and that we can collectively reduce the GHGs through reduced meat consumption.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".