Do good, goes bad, gets ugly: Kony 2012
Bibliographic record
Abstract
With millions of videos with different messages uploaded per year, companies are increasingly looking for means of making their messages stand against competitors. A theory of viral marketing is used to analyze and understand the spread of—and reactions to—a controversial political mega‐viral video, Kony 2012. Through this analysis, policy makers and marketers could gain a better understanding of how they can use mediums such as YouTube to extend their messages. Kony 2012 concerns the highly publicized leader of a Ugandan guerrilla group, Joseph Kony. The video was a call to action and an attempt to educate the world about the atrocities committed in Sudan. The video was made by an organization called the Invisible Children and created by filmmaker Jason Russell. Following the extraordinary success of Kony 2012, Jason Russell was infamously arrested in San Diego for indecent exposure. The story and video of Russell's arrest and breakdown similarly went viral. The framework that follows analyzes the virality of a political video and the downfall of its creator. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".