Zooming in on Lived Experiences: Using Descriptive Phenomenology and Qualitative Methods to Examine Connection to Nature
Bibliographic record
Abstract
During the COVID-19 pandemic, in-person research was prohibited in Canada, necessitating remote research collection and the use of virtual fieldwork, including conferencing platforms. In my qualitative descriptive phenomenological study, I aimed to understand the essence of the connection to nature and the role of awe within that connection; the detailed results are available elsewhere (McCLARTY, 2021). In this paper, I describe how I combined the methods of participant-driven photo-elicitation (PDPE), online video interviews (via Zoom), and descriptive phenomenological research. I provide an overview of participant recruitment, ethical considerations, and data collection, then present reflections on methodological innovations and offer practical insights for researchers considering PDPE and online interview methods for research using a descriptive phenomenological approach. I suggest that PDPE aligns with a descriptive phenomenological method, as it is possible for participants to portray a rich description of their lived experience of a phenomenon.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".