Digital Stories as Data: An Etymological and Philosophical Exploration of Cocreated Data in Philosophical Hermeneutic Health Research
Bibliographic record
Abstract
Many research methods emphasize procedures to minimize potential bias introduced by measurement tools, environmental factors, and researchers themselves. Using the example of digital storytelling, in which short films are cocreated between a researcher and participant, we examine the possibility of cocreated stories as data in philosophical hermeneutic (PH) health research. The etymological explication of the words “data” and “story” brings the meaning of these words closer together while an exploration of the ontological and epistemological assumptions of PH indicate that cocreated digital stories can be viewed as data in a similar way to traditional verbatim interview transcripts used in other types of qualitative health research. Using digital storytelling as a data generation tool in PH health research may help provide a deeper understanding of health-related phenomena by cultivating understanding through genuine conversation, addressing the challenges of language, and apprehending the immediacy of understanding.
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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 | 0.040 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.089 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| 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".