Network from Home : An alternative to Internet domestication, the case of Montreal
Bibliographic record
Abstract
Memories, yours and mine, are stored in an almost invisible blank facade building. Data centers hold raw information to be processed by private companies. This began with the domestication of the Internet in the 1990’s, when telecommunication companies were off to capitalize on the Internet’s access and its storage – the key infrastructural components of the domestic Internet. Service providers have the power redline neighborhoods, while server hosts hold and sell private information. Network from Home proposes responsible redomestication as a critique of aggressive Internet profiteering. It posits an awareness of changing domestic behaviors through Internet phenomenology at home through bits and pieces studied in our theoretical statement Extremely Online: Home in the Advent of the Digital. The proposal uses Montreal, Quebec, to exemplify changes within and outside the home for data sovereignty, culling system redundancies, and democratizing access. This is addressed at the level of legislative agency, educational resources, cultural symbiosis, energy efficiency, and thermal comfort. The project balances a super-peer network master plan with the analogy of carrying our devices like IVs at home, creating an intricate web of small changes in Montreal at various scales, from client to server and the corridors in between. Ultimately, Network from Home envisions a near future where the Internet serves the community equitably, fostering a digitally sovereign and culturally enriched Montreal.
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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 | 0.020 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".