Bibliographic record
Abstract
This introductory chapter examines arguments for and against adopting the All-Affected Principle (AAP) as a criterion for democratic inclusion, and the alternatives. For many, the attraction of the AAP lies in its straightforward simplicity: If you are affected by a collective decision, you should be able to influence it. Yet there remains sharp disagreement among scholars of democracy about how to best formulate the AAP and the circumstances in which it applies. Surveying the literature, we argue that appeals to the AAP will vary according to: (1) organizational scope; (2) decision-making context; (3) kinds of influence; (4) how influence is allocated; (5) the definition of “affectedness”; and (6) the stringency of any participatory requirements. Whether the AAP is consistent with existing arrangements, or requires a more radical redrawing of democratic boundaries, is a question on which opinions may differ significantly. We conclude by discussing the trade-offs between more versus less ambitious versions of the AAP, the implications for addressing pressing governance challenges, and the future of the democratic project more generally.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".