The Need for Human Factors in the Sustainability Domain
Bibliographic record
Abstract
Curbing the over-harvesting of the earth's resources by the developed and developing world cannot be achieved solely by technological solutions. This paper reviews the literature on how reductions in energy consumption can be achieved through behavioral interventions. The literature shows that feedback, a consequence intervention, has been shown to be more effective than antecedent interventions in correcting erroneous heuristics and biases as well as encouraging both efficiency and curtailment behaviors. However, few feedback studies approach the feedback design problem systematically. Human Factors specialists have an opportunity to contribute their expertise in human-machine systems to help address these deficiencies and aid in shifting our societies toward sustainable resource consumption.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 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".