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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.335 | 0.045 |
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".