Telling, Gathering and (Re)Experiencing Stories: Video Interview as Methodology and Innovation in Practitioner-Based Research
Bibliographic record
Abstract
Abstract This article explores the use of video as both an interview technique for data collection and for research dissemination. Attention is given to ways in which video interviews gathered as part of the Maraea community research project were reframed in the development of a virtual reality experience. Maraea is a research project conducted between 2017–2020. A comparative methodology was used to gather knowledge on indigenous-health-practitioner-led community-based solutions from New Zealand, Australia, Canada and the USA that focus on whānau rangatiratanga / family self-determination. Although the video interviews have been disseminated through an official Maraea website, the authors explored ways to reframe or (re)experience the stories being told. An immersive virtual reality ( vr ) prototype was created to explore the potential to bring the user / learner / audience closer to the narrative, to the practitioner telling their story and therefore to the practices being shared.
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 | 0.029 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".