An Episode of Your Life: Rich narrative engagement with episodic stories
Bibliographic record
Abstract
This article describes a new practice map, an “Episode of Your Life”, which adapts existing narrative “… of life” practices to an episodic story from a person’s life using metaphors from film and television production. This practice map draws significantly on ideas of “peopling the room” and the Team of Life in order to scaffold safety in imagining the process of telling painful stories through the collectivising of the storytelling process. This practice map specifically does not require that the storyteller tell the story, but rather invites them to imagine how they might tell a story from their life in a way that aligns with their values, hopes and preferred storylines. Some of the significant effects that we discovered were related to the richness of the visual metaphor for adding another layer of possible meaning-making in the storytelling process, and allowing for a “proliferation of what’s possible” in the imagining of the storytelling, such as through the use of time jumps; computer-generated imagery; inviting rich descriptions of preferred relationships, histories and values; and dignifying of stories that otherwise might be left unspoken. Participants were left with a feeling of solidarity and a “safe riverbank” from which to imagine telling their stories.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".