Learning on the Go: Experiences Researching Urban Stewardship Practices Through Walking Interview
Bibliographic record
Abstract
The following paper offers an in-depth, experiential analysis of the walking interview, applied within a participatory action research context. I share both reflection and critique, analyzing my experience conducting two walking interviews with stewards of urban green spaces in Vancouver, Canada and Medellín, Colombia that explored practices of care in urban nature as well as relationships to local urban ecologies. Discussion is oriented towards two essential methodological questions: (1) how does the use of walking interview advance research towards deeper understandings of stewardship practices and the relationships between stewards and urban nature; and (2) what is the lived and affective experience of conducting a walking interview as a researcher? I adopt a reflective and narrative style to emphasize the role of embodiment in community-engaged work and make explicit the discomfort and uncertainty inherent to qualitative and relationship-centered approaches to inquiry. My intention is to share lessons learned with scholars interested in pursuing similar research approaches. First, I introduce my work, myself, and my relationship and orientation to place-based qualitative inquiry. Next, I share accounts from two walking interviews held with urban green space stewards in Vancouver, CA and Medellín, CO. My experiences with walking interview illuminate its capacity to invite in-depth, sensory connection to place on the part of both the researcher and interviewee. I demystify the dynamics present between researcher and interviewee in the context of action research – commenting on how I navigated fluctuations from outsider to insider researcher (and back) and how negotiation of research relationships influenced my interview practice. I end with reflection on several limitations of the walking interview method, focusing on the challenge of navigating personal attachment and mutual obligation within the container of walking interview.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".