The Development of Emotional Competency through the use of Aboriginal Literature
Bibliographic record
Abstract
The Emotional Competency Through the Use of Aboriginal Literature Project explored the development of emotional competency with students from cross-cultural backgrounds. The project focused on developing teachers’ abilities to plan and create learning opportunities for students to engage with the emotions of characters in Aboriginal literature as a means of developing their own emotional skills. In addition, the project created an understanding of how emotions are developed into values and enhanced respect for the diverse value systems represented by Aboriginal and non-Aboriginal students in the classroom as an aspect of emotional competency. The teachers attended a one-day workshop on emotional competency with an introduction to Aboriginal literature. Teachers were provided with an emotional competency curriculum development guide to help them apply the principles of emotional development to their lesson plans. In addition, they were provided with an introduction to the methods by which literature informs identity. This article will focus on the initial findings of the project. We will examine the implementation of the six principles of emotional competency into classroom practice. The article examines the difficulties and successes of using Aboriginal literature to develop emotional competency and explores the effect the project had on the emotional and identity development of those involved in the project.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.014 |
| 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".