Implementação do novo ensino médio: Compreensões dos docentes de uma escola estadual da região norte de Santa Catarina
Bibliographic record
Abstract
This study presents the understandings of teachers from a public school in the northern part of Santa Catarina state, which implemented the new legal requirements for high school in 2021, offering the integrated formative pathway in the professionalizing track of Administration and Data Science. These understandings were expressed during discussions held in training and curriculum re-planning meetings, which took place weekly at the school during the first quarter of 2022. Data generation was based on the records of the school's minutes and in-service training activities, reflecting the teachers' impressions based on the readings and studies conducted collectively. For data analysis, the study was grounded in the Policy Cycle approach by Ball, Maguire, and Braun (2012; 2016; 2021), with support from Mainardes (2006; 2018) and Gandin (2020). The data were produced and collected documentarily through the reading of the minutes recorded in the school files, which captured the different impressions of the teachers about the New High School curriculum. These impressions not only reflect different understandings of the NEM policy but also influence its developments in the daily school routine. There are professionals who view NEM as a possibility for change, challenges, and an entry point into the job market, while another group of teachers views NEM as rigid, aimed at reinforcing minority interests, weak, divergent from reality, and unstructured.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".