Classical Myth in the University: A Contribution to Professional Teacher Development
Bibliographic record
Abstract
The use of Classical Greek myth as a narrative and metaphorical tool can contribute to the construction of a professional teaching identity. Adopting a biographical narrative approach, the present study sought to assess this contribution in a group of teacher and researcher trainees undertaking a postgraduate university course. The construction of personal narratives used for collective interpretation by the participants that generated them was analysed and interpreted in relation to the development of teacher professionalism. Our findings show the effective activation of metacognitive processes in order to rethink teacher professionalism from a narrative point of view. Using the structure and content of Classical myths as a scaffold, participants established valuable reflections on crucial aspects of teaching, identifying personal achievements and conquests as well as fears and insecurities. The structures latent in myth provided an effective framework with which to project and identify at least three hermeneutical themes—symbolism, function and structure—that form constituent elements of professional identity and are not only intertwined but are also constituted within a community of practice. Thus, Greek myths continue to offer an interesting cognitive and emotional scaffold that contributes to teacher professionalism, facilitating the formulation of a reflective, collaborative and personal meaning of identity which brings together personal teaching experiences and knowledge and is necessarily shared with the surrounding community of practice.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".