The Kuh-Ke-Nah broadband governance model: How social enterprise shaped internet services to accommodate indigenous community ownership in Northwestern Ontario, Canada (circa 1997 to 2007)
Bibliographic record
Abstract
This thesis articulates how the Kuh-Ke-Nah network (K-Net) shaped broadband development in remote indigenous communities. K-Net operates under the not-for-profit stewardship of Keewaytinook Okimanak (KO) Tribal Council. Located in Northwestern Ontario, KO brought K-Net to life amongst its six member First Nations in the mid 1990s. As K-Net evolved and expanded its membership, KO established a governance model that devolves network ownership and control to community networks in partner First Nations. This governance model reflects KO’s use of social enterprise to organize K-Net’s community-based broadband deployment amidst necessary partnerships with government programs and industry players.\nK-Net’s social enterprise has rapidly grown since 1997, when its core constituents fought for basic telephone service and internet access in Northern Ontario. In the space of less than a decade, K-Net communities have gone from a situation in which it was common for there to be but a single public payphone in a settlement, to a point where over thirty now have broadband internet services to households. Technologies now under K-Net control include a C-Band satellite transponder, IP videoconferencing and telephony, web and email server space, and a variety of terrestrial and wireless links that effectively connect small, scattered First Nations communities to each other and the wider world. \nK-Net’s governance model encourages member communities to own and control community local loops and internet services under the authority of a local enterprise. Community ownership and control over local loops allows First Nations to collaborate with KO to adapt broadband applications, such as telemedicine and an internet high school, to local challenges and priorities. K-Net’s aggregation of demand from disparate users, within and across member communities, creates economies of scale for the network’s social enterprise, and allows a dynamic reallocation of bandwidth to meet social priorities. \nBased on four years of research with K-Net stakeholders under the Canadian Research Alliance for Community Innovation and Networking (CRACIN), my thesis documents the evolution of K-Net’s governance model as a reflection of its social enterprise. Drawing from Community Informatics and the Ecology of Games, I trace K-Net’s history and organization to assess how KO, its partners, and K-Net’s constituents, cooperated to make social enterprise viable for member First Nations.
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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 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".