CWIRP Final Report: ICT Infrastructure as Public Infrastructure – Connecting Communities to the Knowledge-based Economy & Society
Bibliographic record
Abstract
This report provides a summary of findings from the Community Wireless Infrastructure Research Project. This research investigated the development of public broadband infrastructure, and was conducted from April 2006 to March 2008 by a team of researchers from Ryerson University, York University and the University of Toronto.The specific questions that guided our research were as follows:• What is the rationale for publicly-owned and/or controlled ICT infrastructure?• What examples of public ICT infrastructure exist in Canada today?• What are the different models and best practices of public ICT infrastructure in terms of deployment, technology choice and innovation, investment, governance, adoption and use?• What are the public benefits of community-based/public ICT infrastructure provision?• What public policies and supports are necessary to promote and sustain public ICT infrastructure?We addressed these questions through case study work with our research partners (The City of Fredericton, Île Sans Fil in Montreal, K-Net and the Lac Seul Wireless Network in North Western Ontario, and Wireless Nomad in Toronto), as well as through extensive study of the broader context for public ICT infrastructure development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".