‘You need to change how you consume’: ethical influencers, their audiences and their linking strategies
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
Our paper advances a subcategory of influencers who mobilise their audiences towards consumption-driven change; we label them ‘ethical influencers’. Using netnography and an archival dataset on ten ethical influencers, we delineate their unique challenges and positioning. Ethical influencers legitimate their accounts via a close-up of personal practices, as opposed to an articulated persona, and connect with divergent audiences to advocate for the needed change. Our paper describes the divergent audience groups and engagement styles: allies, inquisitives, detractors, and enigmatics. We also identify the ethical influencers’ linking strategies to connect these audiences with other market actors (e.g. ethical businesses and other ethical influencers) which include acting, humanising, framing, pivoting, and evangelising. This research advances influencer marketing literature and offers important managerial and public policy implications.
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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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