Bibliographic record
Abstract
Helping people learn to adopt more pro-social lifestyles usually involves persuading them to take new, beneficial actions. However, certain pro-social goals, such as achieving environmental sustainability, also require people to stop performing harmful actions—people are commonly instructed to drive less, use less electricity, and otherwise reduce the amount of resources they consume and waste they produce. In order to help people adopt this potentially unintuitive form of behavior change, we introduce a theoretical framework for the concept of "negabehaviors." A negabehavior is a manner of conducting oneself that supplants undesirable actions—that is, the behavior of not performing specific, undesirable actions. Negabehaviors are a variation on the idea of "negawatts" (a unit of energy saved through conservation), and offer a way to view and teach environmental sustainability that focuses on subtractive elements rather than additive ones. In this paper we present a framework and theoretical grounding for understanding negabehaviors. We discuss the relationship between negabehaviors and environmental sustainability, describing potential ways that this concept can be used in formal and informal sustainability education. By placing an emphasis on actions people need to stop taking, we can make it easier to encourage people to live more sustainable lives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".