Smart community networks: self‐directed team effectiveness in action
Bibliographic record
Abstract
Purpose The purpose of this research paper is to study the governance of smart/intelligent community projects through an analysis of the level of team effectiveness of collaborative telecommunication networks. Design/methodology/approach The research is based on a census of all Canadian smart community projects. A high‐performance team effectiveness instrument identified, through a performance score, whether smart community teams (board of directors or steering committees) are functioning as high‐performance teams. A total of 76 networks were found and 28 responded. Each network is managed by three to nine board members and therefore the researcher received 72 valid questionnaires. Findings Teams, in highly innovative and transformational environments, and involving a variety of community stakeholders, face more challenges in their ability to perform as a high‐performance team. They tend to perform reasonably well in assigning roles and goals, but are having more difficulty managing feedback, establishing a good structure, solving problems and managing relationships. Practical implications Smart/intelligent communities are reuniting several organizations to improve their community or region in social and economic terms. Their level of effectiveness could impact the achievement of group goals and thus impact all citizens within their geographic area. Originality/value The research provides additional information on the weaknesses that smart/intelligent communities are facing in managing their teams, which could lead to better solutions for network governance and collaboration within a multi‐organizational structure.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".