Bibliographic record
Abstract
My aim in this chapter is to conduct a critical analysis of the relationship between democracy and recognition. To date, many discussions of this relationship have focused on a relatively narrow range of issues. Above all, they have been concerned to establish whether — and, if so, how — a democratic polity should guarantee the representation of certain social groups and/or categories of person. Cases often discussed include quotas for women, special representation rights for national minorities, and representation for marginalized and oppressed groups (Kymlicka, 1995; Phillips, 1995; Williams, 1998; Young, 1990, 2000). I would argue that, in at least some cases, these discussions are based on prior but unarticulated assumptions about the relationship between democracy and recognition. One such assumption is to do with the objects of recognition. Does the idea of recognition only concern the status of collective groups, or does it also concern the standing of individual citizens? Another assumption relates to the scope of recognition when evaluating democracy. Should a standard of recognition only be used to evaluate the fairness of democratic procedures, or can it also be used to judge the fairness of outcomes? A third assumption concerns the relationship between recognition and democracy. Are these two political goods always perfectly compatible, or can they come into conflict? And, if they can pull in different directions, then how are such conflicts to be resolved? I would argue that it is only when satisfactory answers to these and other questions have been provided that we can move on to address more substantive issues about the institutional arrangements necessary to achieve democracy and recognition in practice. It is for this reason that I intend to focus in this chapter on a number of fundamental assumptions about the relationship between democracy and recognition. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".