Reconceptualising political influencers: An alternative means of definition and analysis
Bibliographic record
Abstract
Recent interest in online political influencers has resulted in an array of competing definitions of who counts as a political influencer. Contending the value of a more porous and resilient definition able to recognise a spectrum of online political influence, we interrogate scholarship on opinion leadership, influentials, micro-celebrities, and social media influencer studies to reveal a range of identifying traits that can characterise different types of political influencers. Introducing a new approach to categorising these varied manifestations, we discuss six key attributes: personalised communication, compensation, audience size, political topical focus, control, and formal political role. Showing how this approach can be deployed to capture different manifestations of political influencers, we aim to build understanding that is resilient to change over time and that can support comparative empirical work.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".