Filming in the home: A reflexive account of microethnographic data collection with family caregivers of older adults
Bibliographic record
Abstract
This article explores the impact of the researcher’s reflexivity on the data collection and analysis process in the context of a videographic study of home-based family caregivers of older adults. Going beyond a discussion of the role of the researcher’s subjectivity, the article builds on current literature by exploring how the researcher’s embodied self-reflexivity can be used to enrich video based research. The article addresses the researcher’s personal social location and shifting roles throughout the study and how these impacted on her work with the camera, her moment to moment ethical decisions and her perceptions of the participants’ realities. The author illustrates, through the use of journal and transcript excerpts, how the dynamic relationship between the researcher, the participants and the camera creates overlapping and complementary layers of information that together form a cohesive portrait of the action. Throughout, the article discusses the contribution of reflexivity to both the creation and resolution of ethical tensions in the research space.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".