Bibliographic record
Abstract
Democracy is about collective self-rule under conditions that afford everyone political standing and consideration in matters of common concern. But in today’s globalized world, democratic states must respond to a growing number of demands for inclusion from beyond their borders, on issues ranging from migration, to trade, to human rights and the environment. Under these conditions, there is an urgent need for a principled means of determining who is entitled to inclusion, and on what basis. Defenders of the All-Affected Principle claim that inclusions should track the impacts that decisions can have on people’s lives. Defenders of the All-Subjected Principle adopt a similar strategy but use a narrower threshold for inclusion. My argument is that neither principle entirely satisfies. The problem is that both principles are too backwards looking. I offer an alternative formula for democratic inclusion that captures the underlying wrong to which complaints about undemocratic exclusion are seeking to draw our attention. One complaint is about domination: being exposed to the arbitrary and one-sided power of others. Another complaint is about usurpation: having your judgement displaced, without your consent. Using these two complaints as a guide does a much better job explaining when inclusion is justified and the appropriate institutional response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.020 |
| 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".