Local Heroes, Narrative Worlds and the Imagination: The Making of a Moral Curriculum Through Experiential Narratives
Bibliographic record
Abstract
Concern about the impact of narrative worlds and their heroes offered by the media prompted research on encounters with moral models in experiential, narrative curricula. Researchers tracked the extension of a mandated Language Arts curriculum on “heroes” through the experiential narratives of four local heroes chosen collaboratively by teacher, students and researcher. They also elicited and analyzed responses from students to these narrative presentations in order to explore how students understood the narrative worlds presented to them. Instead of focusing on the personalities of the speakers, the researchers considered the experiential stories, and the moments of narrative encounter they offered, as the sources of immediate moral impact. However, this impact, it is suggested, did not adhere to a particular narrative in an undifferentiated manner. Instead, effects varied according to what a particular student brought to the encounter and how he or she was able to experience it. Material from two students’ responses illustrates how they brought their own personal and socio-cultural contexts to the encounter, activating existing dispositions and reinforcing inclinations to behave in certain ways. There was some evidence that the students reconstructed the meaning of events in their lives, were able to interpret their environment in new ways, and constructed visions of possible futures based on this curricular experience.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".