Bibliographic record
Abstract
Online health-related philanthrotainment consists of videos displaying generosity to others and presented in an entertaining way. This practice includes stunt philanthropy where recipients are surprised with a gift and their shock and gratitude are captured for viewers. It can also entail more traditional health-related philanthropic activities including direct payments for medical services like cataract surgeries and public health-related activities such as developing access to clean drinking water. These videos use advertising revenue, product placement and viewer donations to fund the production of the video and support for recipients. While health-related philanthrotainment can confer substantial benefits on the people they feature, it also raises serious ethical concerns. These concerns include the inefficient use of donations and other funding; undermining democratic priority setting in giving; failing to identify and address the root causes of need; encouraging moral licensing behaviour; and degrading and exploiting recipients. In principle, these ethical concerns can be addressed by prioritising the aim of philanthropy over entertainment, including the voices and priorities of viewers and recipients in the production of these videos, and taking lessons from successful education-oriented online communicators. In practice, the twin incentives of attracting viewers and advertisers make these changes at best difficult to implement.
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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 | 0.053 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".