The Use of Message Framing in the Promotion of Environmentally Sustainable Behaviors
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 use of message framing, a technique that shapes perceptions of the outcomes of the promoted behavior, in combination with a specific target audience can substantially enhance the success of social marketing campaigns. Although the persuasive effects of message framing have been widely publicized in the field of social and cognitive psychology, there is a surprising dearth in the literature regarding the role of message framing as a strategy within the context of social marketing to influence environmentally sustainable behaviors. This article provides an overview of the main principles of message framing, including gain and loss framing as well as social and physical threat. The most effective combination of frame and threat may in fact depend on the measure used to assess its influence on behavior. In particular, the literature suggests that the effect of frame and threat interaction may be most prominent in changing attitudes toward the behavior. Four factors should be considered in the use of framing and threat in message design, including: (1) level of risk involved in uptake of the behaviour, (2) degree of self-referencing or self-other referencing in the message, (3) level of experience and knowledge of the target audience and stage of change of the target audience, and (4) gender of target audience. Thus, proper segmentation of the population should be carried out before designing messages with frame/threat factors. Further research on the influence of message framing and the role of audience segmentation in behavioral change strategies is needed to deepen our understanding of its effectiveness in designing social marketing campaigns that focus on environmentally sustainable behaviors.
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.
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 | 0.002 | 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