The Canadian Research Alliance for Community Innovation and Networking (CRACIN): A Research Partnership and Agenda for Community Networking in Canada
Bibliographic record
Abstract
The Canadian Research Alliance for Community Innovation and Networking (CRACIN) is a collaborative partnership amongst academic researchers in Canada, international researchers in Community Informatics, the three principal federal government departments promoting the "Connecting Canadians" agenda, and community networking practitioners in Canada. CRACINs substantive goal is to review the progress of community-based information and communications technology (ICT) development in the context of Canadian government programs promoting the development and public accessibility of Internet services. Central issues to be explored include the sustainability of community networking initiatives, along with an examination of how the Canadian community-based initiatives contribute to: the amelioration of "digital divides"; the enhancement of economic, social, political and cultural capabilities; the creation, provision, and use of community-oriented learning opportunities; and the development of community-oriented cultural content, open source software, learning tools and broadband infrastructures. The over-arching goal of our research is to begin the systematic documentation and assessment of the development of community-oriented ICT capacity and services contributing to local learning, to the strengthening of relations in and between communities, and more generally to community-focused social and economic development in Canada.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".