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Record W2891573811

Leitura contra o fascismo na era do Trump

2018· article· pt· W2891573811 on OpenAlexaff
Henry A. Giroux

Bibliographic record

VenueRevista Internacional de Formação de Professores · 2018
Typearticle
Languagept
FieldArts and Humanities
TopicArts and Performance Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsImmediacyInterpretation (philosophy)Neoliberalism (international relations)ContemplationReading (process)Agency (philosophy)PoliticsSociologyCritical readingDemocracyIsolation (microbiology)Political scienceLawEpistemologyAestheticsPhilosophySocial science
DOInot available

Abstract

fetched live from OpenAlex

This article explores reading as both an act of interpretation and an act of resistance in an age in which neoliberalism undercuts the possibility of contemplation, critical thought and collective action. The article makes a case for reading as a practice that engages historical memory, a slowing down of time, a comprehensive understanding of politics, and as a precondition for individual and collective agency.  Giroux argues that against a numbing indifference to the rise of fascism in the United States, it is hard to imagine a more urgent moment for developing a language of critique and possibility that would serve to awaken our critical and imaginative senses and help free us from the tyrannical nightmare that has descended upon the United States under the rule of Donald Trump. In an age of social isolation; information overflow; a culture of immediacy, consumer glut and spectacularized violence; reading critical books and other representational texts coupled with thinking analytically remain necessary if we are to take seriously the notion that a radical democracy cannot exist or be defended without informed and engaged citizens.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0090.041
Scholarly communication0.0120.013
Open science0.0010.006
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.031
GPT teacher head0.295
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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