“We Were On the Outside Looking In” MyKnet.org: A First Nations Online Social Network in Northern Ontario
Bibliographic record
Abstract
In this article we provide a preliminary account of MyKnet.org, a system of personal homepages for remote First Nations users in Northwestern Ontario, Canada. This free of charge, free of advertisements, locally-supported online social network serves over 40 remote First Nations communities and provides a unique perspective on online social networking. MyKnet.org is comprised of over 30,000 homepages, notable since half of the region’s population of 25,000 is under the age of 25. Through ethnographic methods, seeking to stay as close to the lived experience and cultural practices of MyKnet.org users as possible, we draw upon encounters with a range of developers and users of this network to understand how this community-developed and communitycontrolled form of communication supports activities in the remote First Nations. Our focus is on the importance of the locality of the network: its development within a regional First Nations computerization movement, its strong community-focus, and the central role users play in shaping the form/content of the homepages. Unlike commercial online networks, MyKnet.org is explicitly community-based, not-for-profit, and community-driven, thus playing an important role in local inter- and intra-community interaction in a region that has lacked basic telecommunications infrastructure well into the millennium.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".