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Strategies of Justice

2019· book· en· W4247051839 on OpenAlexaboutno aff
Burke A. Hendrix

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsInjusticePoliticsIdeal (ethics)NormativePower (physics)Economic JusticeState (computer science)SociologyPolitical scienceFace (sociological concept)Action (physics)Law and economicsPolitical actionPolitical philosophyLawEnvironmental ethicsSocial science

Abstract

fetched live from OpenAlex

Abstract Political theorists often imagine themselves as political architects, asking what an ideal set of laws or social structures might look like. Yet persistent injustices can endure for decades or even centuries despite such ideal theorizing. In circumstances of this kind, it is essential for political theorists to think carefully about the political choices normatively available to those who directly face persistent injustices and seek to change them. The book focuses on the claims of Aboriginal peoples to better treatment from the United States and Canada. The book investigates two intertwined issues: the kinds of moral permissions that those facing persistent injustice have when they act politically, and the kinds of transformations that political action may bring about in those who undertake it. The book argues for normative permissions to speak untruth to power; to circumvent or nullify existing law; to give primary attention to protecting one’s own community first; and to engage in political experimentation that reshapes future generations. The book argues that, when carefully used, these permissions may help political actors to avoid co-optation and self-delusion. At the same time, divisions of labor between those who grapple most closely with state institutions and those who keep their distance may be necessary to facilitate escape from persistent injustice over the long term.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.388
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.094
GPT teacher head0.376
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations40
Published2019
Admission routes1
Has abstractyes

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