Digital Ethnography as Planning Praxis: An Experiment with Film as Social Research, Community Engagement and Policy Dialogue
Bibliographic record
Abstract
Stories and storytelling are part of a post-positivist paradigm of inquiry influenced by phenomenology, ethnography and narrative analysis, along with the evolution of visual methods in social research. New information and communication technologies today provide the opportunity to explore storytelling through multimedia, including video/filmmaking, in what we describe as digital ethnography. While there has been a tradition in the planning field of using film for advocacy purposes since the 1920s, we argue for a new direction informed by collaborative planning theory and situational ethics. This paper reports on a three-year, three-stage research project in which we experimented with the use of film as a mode of inquiry, a form of meaning making, a way of knowing, and a means of provoking public dialogue around planning and policy issues (in this case, community development and the social integration of immigrants). We explored the expressive as well as analytical possibilities of film in conducting social research and provoking community engagement and dialogue, taking advantage of the aesthetic and involving dimensions of film as narrative. The research question was a socio-political one: how do immigrants become integrated into a specific social fabric, and how do they acquire a sense of belonging? The site of the research was a culturally diverse neighbourhood in the city of Vancouver, and the specific focus was a place-based local institution, the Collingwood Neighbourhood House. The paper concludes with critical reflections on the use of film in this research project, focusing on ethical issues, power relationships, insider/outsider dilemmas, and reciprocity.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".