www.mcgill.ca/urbanplanning/mpc Using Social Network Analysis to Study Participation in the Community-University Partnership Megaprojects for Communities
Bibliographic record
Abstract
This research studies the participation of community organisations, university faculty, and researchers in a community-univer-sity partnership. The goal of this research is to examine the relationship between the participants involved in the Megaprojects for Communities partnership. The focus is on particular themes in participation including group membership and leadership, community representation, relationships of trust, and expectations and interests of members. Data is generated using litera-ture and document reviews as well as interviews and questionnaires. A social network analysis is applied to the data to visual communication and the flow of resources throughout the partnership. Results of the research assist the self-evaluation and reflection for participants of Megaprojects for Communities. They also add to academic literature on participation in commu-nity-university partnerships and the aptness of social network analysis to study a distinct form of community collaboration. Cite as Pitt, Mary. (2008). “Using Social Network Analysis to Study Participation in the Community-University Partnership Megaprojects for Communities”. Method Report CE08-02E. Montréal: CURA Making Megaprojects Work for Communities-Mégaprojets au service des communautés. More reports and working papers at www.mcgill.ca/urbanplanning/mpc/research/reports
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".