O real e o imaginário: a literatura de ficção científica para o ensino de questões ambientais entre licenciandos e licenciandas
Bibliographic record
Abstract
The real and the imaginary: science fiction literature for teaching environmental issuesScience fiction literature has been considered as a methodology for science teaching for some time.Thus, the assumption of this research development was to study the science fiction literature as a basis for new methodological perspectives for teaching of environmental sciences in particular, considering that future teachers may reflect on how this literature supports the overcoming of the fragmented way of working environmental issues in school, especially in Science and Biology, so that they develop a professionalism committed to raising awareness about the environmental crisis we live in today, since the teaching and learning of environmental sciences are benefited by the contextualization, awareness and motivation to learn when provided by science fiction narratives.Therefore, the general objective of this research was to analyze how Biological Science undergraduates from a public university in the interior of São Paulo understand the insertion of science fiction literature for teaching environmental issues, evaluating, through participation in a book club, development of didactic sequences and semi-structured interviews, how this understanding of science fiction and its use in teaching is linked to a pedagogical practice for environmental awareness.It was observed that the Learning Community formed by the participating undergraduate students is mostly made up of fiction readers who are at the final level of initial teacher training; they were able to appropriate the understanding of science fiction literature as an effective methodology for teaching science and environmental issues and also to think about the construction of their own teaching practices articulated to particular visions of teaching and detached from the traditional teaching approach, besides being able to think critically about the role they may come to play in Education.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.016 | 0.055 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".