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Record W7120424887

Interests of class fractions and the militaryization of public education in Goiás

2020· dissertation· pt· W7120424887 on OpenAlexaboutno aff
Daniel Riccioppo Cerqueira Ferreira de Oliveira

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2020
Typedissertation
Languagept
FieldSocial Sciences
TopicSocial and Political Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMilitarizationIdeologyPoliticsClass (philosophy)Relation (database)Object (grammar)Reading (process)
DOInot available

Abstract

fetched live from OpenAlex

The present thesis investigated the militarization process of public education in Goiás and its relation to the concrete interests of class fractions. We identified the need for a reading of the theme beyond traditional educational conceptions. The objective was to access and understand the main motivations, interests and the meaning of the search of families that resort to the conditions offered by the militarized school administration model. To this end, we resorted to the concepts, categories, and arguments of Christian Laval, Jessé Souza, Pierre Bourdieu, among others. Based on the thought of the Russian anarchist Mikhail Bakunin (1814-1876) we concentrate our efforts to establish a materialist reading of militarization. The method applied is of an interdisciplinary nature in the composition of a historical and sociological analysis of the object investigated. From a multiplicity of sources and the application of instruments, we have observed multiple determinations that drive the demand for military colleges. In this study we found a set of contradictions between the pro-militarization arguments and the reality of the process. However, even in contradictory terms the dynamics of militarization connects with concrete interests of individuals, indicating that the search is not reduced to the political and ideological aspect, but also for pragmatic purposes. In this sense, we believe that the research proposes a change of emphasis and focus in approaches to the subject, as well as the need for debate.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.007
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.034
GPT teacher head0.309
Teacher spread0.276 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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