A collaborative governance process: The City of Vancouver’s 2014-2017 healthy city for all leadership table
Bibliographic record
Abstract
Collaborative governance is an emerging form of public administration; it can be defined as formal consensus-driven arrangements between government and non-government actors in order to tackle systemic urban challenges.Municipal governments may embark upon a network governance initiative for a variety of reasons such as recruiting expertise, increasing inclusion, or securing public buy-in.How a municipality understands an issue is necessarily tied to the method it selects to address it with.In choosing a collaborative method, the process convener will make preliminary decisions which will have effects on the ensuing proceedings.This research is focused on the City of Vancouver's 2014-2017 35-member collaborative leadership table for its Healthy City Strategy & Action Plan (the municipality's social sustainability plan).It examines the City's reasons for choosing to initiate a participatory process, and the ways in which those strategic aims influenced the configuration and therefore the unfolding of the process.The research conducted as part of this case study was qualitative, multimethod, and involved three sources of data collection: online participant surveys, semistructured participant interviews, and a document analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.007 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".