Multimedia and Urban Narratives in the Planing Proces. Film as Policy Inquiry and Dialogue Catalyst
Bibliographic record
Abstract
This chapter explores some of the potential applications of multimedia in the urban policy and planning fields. This is an epistemological as well as a pragmatic exploration, probing the capacities of multimedia as a mode of inquiry, as a form of meaning making, as a tool of community engagement, and as a catalyst for public policy dialogues. Although this is not a totally new epistemological excavation, we are trying out new tools and developing a new approach, which we describe as digital ethnography, a qualitative inquiry using film and multimedia languages to create a polyphonic narrative. \nOur chapter is a critical reflection on a three-year action research project that began in 2005 as a dual inquiry: it is both policy-oriented and methodologically and epistemologically experimental. In policy terms, we were interested in how Canada’s liberal approach to immigration, and the accompanying national multicultural philosophy, actually translated from the national policy making level to the streets and neighborhoods where diverse cultures face the daily challenges of coexistence. What kinds of sociological and political imagination, at the local level, could make for peaceful coexistence? How were anti-immigrant and racist sentiments being addressed? What would an in-depth study of one culturally diverse neighborhood in Vancouver reveal? Methodologically, could we use film as both a mode of inquiry and as a means to disseminate research findings? And what were the epistemological underpinnings of such a methodology? \n\tWe begin with an account of our postpositivist epistemological orientation, particularly our emphasis on polyphonic narrative analysis through the medium of film as an antidote to the typically bidimensional, cartographic, and quantitative biases of urban policy and planning research. We then proceed with a sketch of the back story of Canada’s evolving approach to immigration policy, followed by our case study, a thick description of the role of one local institution in one culturally diverse neighborhood in Vancouver, asking “how do strangers become neighbors?” Interwoven with this inquiry is the account of the making of our documentary Where Strangers Become Neighbours and an evaluation of its effectiveness as a catalyst for policy dialogue.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".