Bibliographic record
Abstract
The main research problem this thesis addresses is that public engagement processes treat actor groups as homogenous as opposed to recognizing their unique differences and their positionality in local issues.The thesis posits that it is these differences that affect their ability to engage on issues of importance to those actor groups.The Vanier neighbourhood in Ottawa serves as a case study for this thesis, as it provides a microcosm into the complexities that arise in an environment comprising different interests, capacities, and capabilities of individuals and organizations.Precision Cities was introduced to this neighbourhood to create an engagement process for the Vanier community to collaborate in finding sustainable solutions capable of tackling food insecurity.The analysis of each actor group demonstrates the importance of creating personalized engagement strategies due to the distinctions between different actor groups in terms of what motivates and demotivates them to take community action.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".