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Record W2890377422

Rethinking Recognition: Freedom, Self-Definition, and Principles for Practice

2018· article· en· W2890377422 on OpenAlexaboutno aff
Caitlin Mun Cheng Tom

Bibliographic record

VenueeScholarship (California Digital Library) · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInjusticePoliticsNormativeGovernment (linguistics)SociologyValue (mathematics)Political scienceEpistemologyLawPhilosophyComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.088
GPT teacher head0.247
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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