Transformative Influence? The Hedonic and Eudaimonic Sustainabilities of Social Media Influencers
Bibliographic record
Abstract
Amid a global context of intensifying climate-and-ecological crisis, academics, NGOs, and governments alike have called on social media influencers to support sociocultural change for sustainability by promoting more sustainable ways of living. Yet, given that influencers depend on social network sites that profit from existing sociopolitical structures and systems of consumption, it is unclear whether their portrayals of sustainable lifestyles involve political efforts to change systems, actions that signal anti-consumerist values, or only incremental changes in the private actions of individuals. This research asked: what elements of sustainable lifestyles do influencers promote, and how do they portray the desirability of these lifestyles to their audiences? Through a qualitative study that draws on conceptions of transformative change for sustainability, pro-sustainability values, and subjective wellbeing, we found that some influencers are promoting private pro-sustainability actions in ways that signal both pleasure-based (hedonic) and principle-based (eudaimonic) values. However, features of social network sites such as online hate and shadowbanning pose barriers to influencers promoting civic and political practices that are integral to sustainable lifestyles. Addressing these barriers will be key to enabling influencers’ support of the widespread action necessary to advance transformative change for sustainability.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".