Collaboration networks in a public service model: Dimensions of effectiveness in theory and in practice
Bibliographic record
Abstract
Abstract We present both researcher and practitioner responses to the results of a 2007 research study originally designed to apply social network theory to the Neighborhood Integrated Service Teams (NIST). The municipal government for the City of Vancouver, British Columbia, Canada designed and implemented the model in 1995 to strengthen community partnerships by connecting city services to the neighborhoods. They defined program effectiveness in terms of reduced citizen problems, with more than a 60% decrease of complaints to City Council by citizens since inception. Social network analysis mapped the relationships of network members and social network theory was applied to the results. Sociograms represent the interactions across and within the networks. The results and analysis demonstrated the pattern of information flows and the behavior of collaboration across the networks. These results were presented to the full NIST membership, NIST coordinator and City Manager for feedback. The research results demonstrate that collaboration network effectiveness extends beyond the realization of one organizational goal, encompassing contextual benefits unrecognized without an examination of the information environment. The combination of researcher and practitioner perspectives provides a more complete view of the information environments within collaboration networks and informs a broader consideration of network effectiveness dimensions.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".