Educação básica e pandemia. Um estudo sobre as perceções dos professores na realidade portuguesa
Bibliographic record
Abstract
Starting from the political options in education in Ibero-America, this article focuses on the analysis of measures taken in Portugal in order to respond to the evolution of the Covid-19 pandemic. From the theoretical-conceptual analysis we highlight the systematic use of online education and the effects that accentuate an educational reality discernable by inequality and exclusion. Based on quantitative empirical study, of quantitative, through the implementation of a questionnaire to teachers of basic education (n=280), data characterizing two distinct phases are presented: the closure of schools with the emergence of the pandemic; the return to face-to-face teaching, from the moment when the pandemic was already under control. The empirical data show that teachers emphasize that online learning leads to inequalities among students and contributes to reduced learning, and that teachers have embraced the innovative measures introduced by digital technologies. Teachers reveal a certain skepticism regarding the social valorization of the teaching profession and the school. As a challenge for the post-Covid-19 curriculum, the importance of education for citizenship, organized in the school according to the search for answers to global and local problems, is highlighted.
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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 | 0.003 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".