Investigating Numbers and Behavior: Grab ‘n Go
Bibliographic record
Abstract
This report investigates the amount of waste generated by Grab ‘n Go and student’s behavior towards the dining option. A survey was taken at both Chapin and Hubbard where 306 and 154 interviews were taken respectively for a 95% confidence level. Students were asked six questions: 1. How many times did you visit Grab ‘n Go a week? 2. Why did you come today? 3. What House are you from? 4. Where did you take the food? 5. Are you concerned about the amount of waste generated? 6. Would you be open to any alternatives, such as Dining Services providing Tupperware? We found that students visited Grab ‘n Go around three times a week, they came because of the menu option, location, and because it was prepackaged. We found that most of the food was taken back to the student’s House, however, in Hubbard’s case a quarter of students sat and ate in Hubbard. Just under half of the students surveyed were concerned about the amount of waste generated, with the next majority being unconcerned, and a small portion being unsure. A large majority of students were open to the option of an alternative such as Tupperware. In general we found a lack of awareness in the amount of waste being generated by Grab ‘n Go and a disconnect between the campus’ commitment towards sustainability and this dining option. We have gathered together some recommendations Smith College can explore in the immediate, mid‐range, and long‐ term future.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".