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Record W3193772815 · doi:10.14393/ufu.di.2021.437

O método de melhoria de resultados na agenda da rede pública estadual paulista: a lógica gerencial na definição da qualidade educacional

2021· dissertation· pt· W3193772815 on OpenAlexaboutno aff
Sabrina Bucci Rosa

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldSocial Sciences
TopicEducation and Public Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesPhysicsPhilosophy

Abstract

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This master's degree research is attached to the research line "State, politics and education management " of the post graduation program at the Federal University of Uberlândia and aims to investigate which conception of quality that the MMR is used in to advamce SARESP indicators and reach the IDESP's goals? Therefore, it was defined as the main research's direction to analyze the conception of quality on the Method of Improvement of Results (MMR) and its relation with the indicators of SARESP and IDESP;, and, as specific aims of this reseach: to understand the process that placed the external evaluation as the main inductor for education politics focused on quality; to analyze which way the indicators from SARESP and IDESP are used on MMR; to to identify in the analyzed documents the managers' role in the implementation of the method; and; understand the conceptions of quality in order to comprehend the conception defended by the MMR.In order to achieve the intentioned aims, based on Bardin (1977), Cellard (2008), Chizzoti (2000), Marconi and Lakatos (2003), Severino (2007), among others, a qualitative reseach was conducted though biographical and documental research and, to anyone the datas' interpretation, a content's analysis. As theory support these following authors were selected: Adrião and Garcia (2014), Afonso (1998, 2002, 2010, 2012, 2013), Dardot e Laval (2016), Dourado, Oliveira and Santos (2007), Freitas (2002, 2011, 2014, 2015, 2016, 2018), Gentili (1996, 1997), Harvey (2005), Laval (2019), Ravitch (2011), Sander (2007, 2009). The analysis of datas provided the comprehension that the MMR, created by Falconi, consult enterprise from the country, integrates the program management in focus, implemented by the state's education secretary of São Paulo in 2017 and intends to provide the improvement on the learning quality; the program is included in a large group of actions implemented in the local education system, aligned with what is nailed by different multilateral organisms and the neoliberal ideal in a context of advance of the school privatization, and the emphasis in evaluation, control and adoption of management principles from the private company system. The method is inspired by the Total Quality and transposes to the interior of technical schools, activities and terminologies that are used in the management of private companies. The results from SARESP and IDESP, as well from the Evaluation of the learning in process, subsidize the steps and stages of the method. Therefore, we realized that the designed management by the MMR is founded on the productivity logic, the efficiency and efficacy with the intention to increase the SARESP's indicators, reducing the school work during the the school year to the reach of the IDESP' target. Furthermore, it assumes a deconcentration as it transposes to the school the decision about what must be done to improve the results in the school, also, it causes a centralisation with the increase of the management control and with what is done by the schools, it intensificates mechanisms of accountability and ranking.We concluded that, although it defends the improvement of the learning, the MMR reduces the real values to measurable values that are quantitative for patterned tests, not considering important factors and dimensions in and out schools that interfere in the education quality.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.412
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.103
GPT teacher head0.421
Teacher spread0.317 · 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.

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
Published2021
Admission routes1
Has abstractyes

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