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Record W6906530649 · doi:10.17605/osf.io/2atk7

The effect of moral appeals on pro-environmental behavior

2024· other· en· W6906530649 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMoralityMoral disengagementMoral responsibilityMoral behaviorSocial cognitive theory of moralityMoral standardsMoral developmentAction (physics)

Abstract

fetched live from OpenAlex

It is a widely known fact that climate change is a manmade problem that has immense repercussions and is affecting us all. Given the major impact that people’s acts and behaviors have on the environment, the only way to combat climate change is by changing people’s environmentally destructive behavior, and making them act more pro-environmentally. However, creating behavior change in people is a difficult task. Therefore, it is important for research to look into different ways that could help to change the behavior of people in a positive way. As established by self-determination theory, motivation, which can be defined as the engagement with goal-directed behavior, is an integral factor for creating behavior change (Deci and Ryan, 2008). Even in the absence of attractive external incentives, intrinsic motivation to combat climate change and its effects has been found to increase people’s engagement with pro-environmental behavior (Balunde, 2023). Appeals to morality could be one way of increasing people’s intrinsic motivation. Moral appeals can be defined as messages aiming to invoke a sense of moral responsibility. Because acting morally and being seen as moral agents is of great importance to people, reminding people of their moral responsibility may have a positive impact on their behavior, as it may make them engage in more pro-environmental behavior (Ellemers and Van Nunspeet, 2020). Therefore, playing into people’s morality might be a good way to increase people’s motivation, thereby encouraging them to act more pro-environmentally. However, the opposite could also be true. As some people may perceive appeals to their moral responsibility as a threat to their identities, such reminders of their moral responsibility might instead lead to counterproductive reactions (Van Nunspeet and Ellemers, 2023). The purpose of this research study is to compare the effect of moral appeals priming individual responsibility and moral appeals priming collective responsibility, on pro-environmental behavior. Participants of the study will perform a time estimation task in which different conditions allow them to receive monetary gains either for themselves, for the Climate Disaster Fund, or to receive no money at all. Before and during the task, participants are shown a text including a moral appeal, either priming their collective or individual responsibility to take care of the climate. Based on their performance in the different conditions, conclusions will be drawn about the effectiveness of the two types of moral appeals to encourage pro-environmental behavior. Additionally, a questionnaire will be used to measure behavioral intentions and responsibility towards the climate. Balunde, A. (2023). The power of environmental considerations to guide pro-environmental behavior among different people and in different contexts: [University of Groningen]. Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology/Psychologie Canadienne, 49(3), 182-185. Ellemers, N., & Van Nunspeet, F. (2020). Neuroscience and the social origins of moral behavior: How neural underpinnings of social categorization and conformity affect everyday moral and immoral behavior. Current Directions in Psychological Science, 29(5), 513-520. Van Nunspeet, F., & Ellemers, N. (2023). Regulating other people’s moral behaviors: Turning vicious cycles into virtuous cycles. Group Processes & Intergroup Relations, 136843022311595.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.045
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0210.001

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.

Opus teacher head0.026
GPT teacher head0.325
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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