Emerging Horizons, Part Seven. Mike's Story: Lessons Learned
Bibliographic record
Abstract
This seventh and final instalment of the Emerging Horizons series brings all the experiences of the participants in the film together with my own (ML) as the digital storytelling (DST) facilitator to discuss three of the many lessons learned from this research project (please see the introductory editorial to the series, Crafting Meaning, Cultivating Understanding, to access the film). The article summarizes the interpretations of the film in both written and digital story form, explores the value of the “artist of our days” mindset that DST can cultivate in Adolescent and Young Adult (AYA) cancer survivors, and leans on hermeneutic philosophy to emphasize the importance of conversation when viewing digital stories. I conclude with a personal exhortation to be vigilant in looking for the “setups and payoffs” in my own life so that, as a DST facilitator working in healthcare settings, I can continue to help others story, and re-story, the meaningful moments of their health experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".