Interests of class fractions and the militaryization of public education in Goiás
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".