Rethinking Recognition: Freedom, Self-Definition, and Principles for Practice
Bibliographic record
Abstract
This dissertation argues that self-definition should be an important guiding value for the politics of recognition and identifies three principles essential to such a politics: self-definition, responsiveness, and internal contestation. Government officials and other authorities who seek to correct social and political inequality with policies of recognition should use the principles I propose to guide their efforts. We should expect injustice to persist even in the face of widespread, honest efforts to practice recognition as justly as possible, but we must still strive for just practices that support the positive potentials of recognition by respecting both equality and freedom. These principles are drawn from a detailed examination of three central examples of recognition in practice. I examine (1) the Canadian government’s 1988 apology for internment and dispossession of Japanese Canadians in the 1940s, (2) the development of the Canadian Museum of Civilization’s exhibits of Aboriginal history and culture from the 1980s to early 2000s, and (3) the development of the National Museum of the American Indian’s inaugural exhibits in the 1990s and 2000s. These practices of recognition illustrate the importance of self-definition and suggest the above principles practice. As a work of contextual political theory, this dissertation develops normative principles by bringing conceptual conversations in the theoretical world together with evocative contemporary political examples.
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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 | 0.051 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.234 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".