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Record W2586494066 · doi:10.20355/c5hg62

Liberating Education: The Dissident Voice of a Good Enough School

2017· article· en· W2586494066 on OpenAlexvenueno aff
Martha Paiva Scárdua, Afonso Galvão

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCritical and Liberation Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsDemocratizationDemocracyPoliticsCitizenshipSociologyForm of the GoodGood citizenshipFocus (optics)PedagogyEpistemologyPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

This study develops a concept of the Good Enough School inspired by Freire’s (1997) Liberating Education and Dussel’s (1977) Philosophy of Liberation in response to policies and practices that reduce the focus of education to a mere performance on national and international tests, and the search for first places on rankings. We criticize educational models that minimize important educational dimensions, such as qualification (learning of formal knowledge), humanization, democratization and transcendentalism, that are essential to the construction of another model of social and economic development. The Good Enough School is based on the ethics of otherness and assumes the importance of decolonization processes in the ways of being, thinking and acting. It is oriented towards dealing with personal, social, local and global challenges through potential spaces (Winnicott, 1975) that ensure the care of children and young people who were not socialized in a good enough environment, and spaces of appearance (Arendt, 2007), which ensure the political exercise of citizenship and democracy to the school community.

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)0.020
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.079
Scholarly communication0.0130.017
Open science0.0020.013
Research integrity0.0090.022
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.432
Teacher spread0.387 · 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 designQualitative
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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

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