El desarrollo de la creatividad emocional en los docentes: conectando la teoría con la práctica
Bibliographic record
Abstract
The present article investigates the formative aspects that contributed (or inhibited) the development of the emotional creativity of three elementary school teachers in Lima, Peru. This paper analyzes the data of one of the participants, Súper Keka, which was the pseudonym she created for herself, as part of the creative process. Based on different creativity theories useful in the educational practice, a phenomenological approach encompasses autobiographical work, using arts-based methods (particularly collage inquiry) as one of the main vehicles for the investigation. This study suggests the importance of collaborative work to promote the transformation of rigid thought paradigms, that are—to an extent—structural, due to the own formative experience of the educators. The, so called, education for the 21st century, demands a substantial revolution of teaching and learning, understanding that the role of the educator is now to awaken and nurture the capacity to learn of learners. To do so, it is necessary that educators revise their own formative experience, learning to un-learn. In this amazing journey of self-discovery, Emotional Creativity is a straightforward and flexible system to develop the aspects that will allow us to transform the current dominant paradigms and develop our resilience and ability to adapt to change.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".