Mapping Government Use of Social Media Influencers for Policy Promotion
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
This study explores how national governments leverage social media through influencer partnerships and digital campaigns to promote cultural values and policy goals. Covering a broad spectrum of governmental bodies (e.g., ministries and officials), the research highlights the variety of influencer–government partnerships and collaborations. The study comes at a time when diverse regulatory frameworks are emerging globally to govern influencers’ activity, mandating transparency in sponsorships, protecting consumer interests, and setting boundaries on influencer involvement in governmental and political campaigns. The methodology combines two main steps: (a) a web search of news articles and blogs to identify relevant examples of government–influencer collaborations; (b) a manual annotation of government-led influencer strategies of the retrieved examples based on thematic areas, degree of autonomy in the partnership, and narrative strategy. The study focuses on France, the US, and Canada, chosen for their advanced digital environments and initiative-taking approaches in both social media regulation and public diplomacy. The main contribution of the study is to develop a typology of government–influencer collaborations to align public perception with (inter)national policy goals and reach their target audiences.
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 | 0.000 | 0.002 |
| 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