Kingdon-Khan Model: Acknowledging the Role of Media, Public Opinion, and Social Movements in Agenda-Setting
Bibliographic record
Abstract
This article proposes the Kingdon-Khan Model (KKM) as an extension of John Kingdon’s Multiple Streams Model (MSM) of agenda setting. While the MSM is comprehensively used to explain how issues reach policymakers’ agendas, it underrepresents the influence of media, public opinion, and social movements on agenda setting. To address this limitation, the KKM introduces a fourth “social stream” encompassing these interrelated societal forces. Drawing on empirical research on media, public opinion, social movements, and public policy, the authors conceptualize components of social stream and its interactions with the problem, policy, and political streams. The authors illustrate the KKM’s utility through examples of the Black Lives Matter and Pro-Palestinian movements. The KKM enhances the MSM’s explanatory power by accounting for the complex, multidirectional forces influencing contemporary agenda setting.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".