Making the connection: a sustainable community network for British Columbia
Bibliographic record
Abstract
The goals of this thesis are to identify the objectives for creating a Sustainable Community Network (SCN) within B.C., to acknowledge the various tools available to facilitate the network, and to develop illustrative models to guide those contemplating the establishment of a network. Three primary research methods were utilized in this project: literature reviews, group discussion in a focus workshop, and individual interviews. The literature reviewed focused on the fields of collaboration, networks in both technical and social capacities, and coalitions. Four objectives are identified as motives to create a SCN: to provide exchange mechanisms, to organise the "unformalized" field, to create a community of interest, and to be a vehicle for power, influence and empowerment. The emphasis at the beginning should be on building personal relationships over creating an electronic network. Other specific products and services are identified as beneficial for the stakeholders: newsletters, conferences, inventories, facilitation, and a clearinghouse for information. Alternatives for administering the network include a network manager, an administrative body, a governing body, and an intermediary broker. This research helps define networks within the field of planning. They may act as a support system, streamline efforts through collaboration or by reducing duplication of effort, act as a forum for monitoring and assessment activities, and be a source for on-going public participation. Three conceptual models are developed representing a range of possibilities for creating the network. The models are labeled the "Fundamental Network" at the basic level, the "Coalition Network", and the "Collaborative Network" at the most complex level. The need for a SCN is reconfirmed. The network should proceed from a "human scale" and develop the capabilities of the electronic network as computer literacy and technological capacity become generally available. Finally, it is recommended that the network should proceed slowly, building on community objectives and incorporating the diverse activities possible through collaboration as experience is gained. Further research is needed to clarify the potential for networks in planning and management, to better understand the evolving place for computer technology, and to monitor the effectiveness of the networks as they are implemented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".