TECHNOLOGICAL DETERMINISM IN CANADIAN TELECOMMUNICATIONS: TELIDON TECHNOLOGY, INDUSTRY AND GOVERNMENT
Bibliographic record
Abstract
This paper looks at the notion of technological determinism as popularly used by government and industry to generate acceptance of and demand for innovation and its products. In particular, it considers how the rhetorical surround of the research, development and market-ing of Telidon may be seen as technological determinism shaping com-munications policy. Ce document Ctude la notion du dtterminisme technologique tel qu7employC gCnCralement par le gouvernement et I'industrie afin de gCnCrer une acceptation et une demande pour l'innovation et ses dCrivCs. I1 s'attache particulikrement au cadre rhttorique de la recher-che, au developpement et au marketing de Telidon pour les envisager comme un indicateur du dCterminisme technologique qui fa~onne la politique des communications.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".