Deception, Disadvantage, and Democratic Deliberation: Aboriginal Strategies and Deliberative Institutions
Bibliographic record
Abstract
Deliberative democrats argue that citizens should ideally enter the public sphere openly and honestly, so that they can reason most effectively with others. Yet deliberative theorists increasingly acknowledge that deviations from this model are necessary in practice, and often endorse rhetoric and related forms of persuasion where necessary to bridge divided communities or spotlight serious injustices. They have given limited attention to the status of deceptive speech, however. In this paper, I consider the ethics of political deception in two ways. First, I consider its acceptability by groups that are severely disadvantaged. Drawing on John Dryzek’s work, I argue that deception is permissible when necessary to build a larger “deliberative system” or to reduce profound injustices. Second, I consider the effects of deception on disadvantaged actors themselves, arguing that deceptive strategies are often harmful because their distorting effects on self-understandings and group solidarity. I thus argue that, for their own good, disadvantaged groups should choose deception only in extreme circumstances, even if they are morally permitted to use it much more frequently. The paper looks especially at the political and legal claims of Aboriginal peoples within Canada and the United States, though its general arguments are more broadly applicable.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".