'Popcorn Tastes Good': Participatory Policymaking and Reddit's 'Amageddon'
Bibliographic record
Abstract
In human-computer interaction research and practice, policy concerns can sometimes fall to the margins, orbiting at the periphery of the traditionally core interests of design and practice. This perspective ignores the important ways that policy is bound up with the technical and behavioral elements of the HCI universe. Policy concerns are triggered as a matter of course in social computing, CSCW, systems engineering, UX, and related contexts because technological design, social practice and policy are dynamically entangled and mutually constitutive. Through this research, we demonstrate the value of a stronger emphasis on policy in HCI by exploring a recent controversy on Reddit: “AMAgeddon.” Applying Hirschman’s exit, voice and loyalty framework, we argue that the sustainability of online communities like Reddit will require successful navigation of the complex and often murky intersections among technical design and human interaction through a distributed participatory policymaking process that promotes user loyalty.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.064 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".