Bibliographic record
Abstract
Where should the role of influencer relations reside within a communication department? The results of this study suggest that that influencer relations is most at home under the public relations (PR) umbrella. To help explain why PR professionals are best positioned to manage the influencer relations functions this author has proposed the JIM (Journalism, Influencer, Marketing) Disclosure Approval Continuum. In addition, this author proposes a conceptual model for understanding the ideal influencer relations work flow (brand to influencer) and non-ideal (agency-mediated). Finally, this author proposes a 12-phase for effective influencer campaigns. The stated goal of this authors research to help communicators and influencers come to understand the role of influencer relations or what she calls “Influencer Logic”. By finding mutual understanding of the rules of engagement influencers and brands will have more harmonious relationships with each other and ideally achieve more mutually beneficial results.
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 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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".