Moving images, Moving Methods: Advancing Documentary Film for Qualitative Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the widespread use of digital media as a tool for documentation, creation, preservation, and sharing of audio-visual content, new strategies are required to deal with this type of “data” for research and analysis purposes. This article describes and advances the methodological process of using documentary film as a strategy for qualitative inquiry. Insights are drawn from a multimedia study that explored Inuit-caribou relationships in Labrador, Canada, through the co-production of community-based, research-oriented, participatory documentary film work. Specifically, we outline: 1) the influence of documentary film on supporting the project conceptualization and collaboration with diverse groups of people; 2) the strength of conducting filmed interviews for in-depth data collection, while recognizing how place and activities are intimately connected to participant perspectives; and 3) a new and innovative analytical approach that uses video software to examine qualitative data, keep participants connected to their knowledge, and simultaneously work toward creating high impact storytelling outputs. The flexibility and capacity of documentary film to mobilize knowledge and intentionally create research outputs for specific target audiences is also discussed. Continued and future integration of documentary film into qualitative research is recommended for creatively enhancing our abilities to not only produce strong, rich, and dynamic research outputs, but also simultaneously to explore and communicate diverse knowledges, experiences, and stories.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.299 | 0.240 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it