Bibliographic record
Abstract
This study presents the understandings of the teachers of a state school in the north of the State of Santa Catarina, which implemented the new legal prerogatives of High School, in 2021, offering in an integrated way the training itinerary in the professionalizing path of Administration and Data Science. These understandings were expressed during discussions held at teacher training and curriculum replanning meetings, which took place weekly at school, in the first quarter of 2022. Records of school unit minutes and in-service training activities were used to generate data. For data analysis, studies were based on the Policy Cycle approach by Ball, Maguire and Braun (2012; 2016; 2021) with input from Mainardes (2006; 2018) and Gandin (2020). The data were produced and collected in a documentary way from the reading of the protocol registered in the school archives, in which the different perspectives on the New High School curriculum expressed by the teachers were recorded. There are professionals who understand the NEM as a possibility for change, challenges and as a gateway to the job market, while another group of professors understand the NEM as something plastered to reinforce minority interests, fragile, divergent from reality and unstructured, the considerations of the latter group being reproduced more frequently among teachers.
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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 | 0.016 | 0.029 |
| 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 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".