Delineating positive spillover, negative spillover, and licencing within the pro-environmental literature
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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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".