Emerging Horizons, Part Three. Kelsey’s Story: Breaking Cancer’s Grasp
Bibliographic record
Abstract
This third installment of the Emerging Horizons series explores Kelsey’s digital storytelling (DST) experience (please see the introductory editorial, Crafting Meaning, Cultivating Understanding, to access the documentary film on which the series is based). In addition to providing a compelling exploration of a relatively common occurrence of Adolescent and Young Adult (AYA) cancer survivors, delayed diagnosis, Kelsey’s involvement in the film illustrated the potential for DST to help participants explore, name, and represent their inner emotional experience. Her storyline illuminated how difficult it can be for AYAs to both understand their “true feelings” and share them with others in a way that moves beyond a surface level, “hashtag” description of emotion (e.g. #sad). I (Lang) conclude by discussing how the three primary modes of narrative engagement in the DST process (external, internal, and reflexive) could help AYAs cultivate a deeper understanding of their emotional cancer experiences, and in doing so, break cancer’s grasp on their life, by grasping it instead.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".