Book Review: Mike Zajko, Telecom Tension: Internet Service Providers and Public Policy in Canada*
Bibliographic record
Abstract
The world of telecommunications, writes Mike Zajko in this timely analysis of internet service providers (ISPs), “is a world many of us have never wondered about, just as we are rarely curious about where our sewage goes or how the electricity grid is configured.” Yet ISPs are not just conduits of fast-traveling light pulses that deliver the internet; they transmit, channel, form, and express a multitude of public policy issues, and they have an important level of agency in the construction and exercise of those policies, too. These dynamics have implications for a large bandwidth of topics relevant to Canadians, including the market, competition, education, socio-cultural development, and security. Applying a sociological framework interwoven with historical and legal analysis, Zajko’s Telecom Tensions describes the internet as contemporary society’s “connective tissue” and examines the ways in which ISPs serve as its “intermediaries,” constituting social relationships of control and power. He provides insight into an infrastructure that is often invisible (the banal fac ̧ ade of 151 Front St. West in Toronto serving as an illuminating metaphor for this lack of visibility), which nonetheless bears heavily on our broader social wellbeing.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".