Delineating positive spillover, negative spillover, and licencing within the pro-environmental literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The theoretical constructs of positive spillover, negative spillover, and licencing have received a wealth of attention over the past few decades, the distinctions between these phenomena are sometimes unclear in the extant literature. Particularly within the domain of pro-environmental behaviour, these terms are sometimes used in different ways, defined in a manner that may lead to different conclusions from different research teams. The current paper provides a framework-based review, examining and synthesising different definitions of positive spillover, negative spillover, and licencing (including moral licencing). Through this framework-based method and definitional analysis, we uncover conceptual distinctions within how each of these terms is defined. We provide new integrated definitions for each term that is broad enough to encapsulate each phenomenon while specific enough to clearly delineate each construct. Thereby, this paper contributes to the extant literature on spillover and pro-environmental behaviour by providing conceptual clarity to a domain mired in ambiguity.
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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 | 0.004 | 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 | 0.000 | 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 it