Rise of the Canadian Network Society
Bibliographic record
Abstract
Current protection acts and legislations set in place for Canadian residents regarding their privacy and securities online are not adequate and must be reviewed. It was investigated whether Canadian residents are aware of how much data is being collected about them while operating in online spheres. Moreover, the lack of protection for Canadian residents regarding data collection and information sharing in the marketing and advertising sphere. The scope of research highlighted this gap and shed light on the importance of consumers' privacy and security. Through mixed methods of both quantitative and qualitative research, a survey was administrated to Canadian residents to examine their knowledge on current legislation regarding data privacy and their overall knowledge of how organizations surveil, collect, and distribute their personal data. It was discovered that Canadian residents are informed of the scope in which their data is being collected, however, are not informed on the full implications of this collection. More importantly, a majority of Canadian resident respondents are ill informed on the current legislations and regulations set in place to protect them. Canadian residents showed immense interest in requesting more information on how they can stop this invasion of privacy in collection practices and voiced that more stringent protocols should be put in place to better regulate this industry. Current laws and regulations regarding the collection of personal data in online sphere are outdated. Furthermore, call the need to be reexamined, updated, changed, and quantified. Although Canadian residents are aware of the data collection being conducted, Canadian residents feel organizations are being misleading and therefore do not feel implicit consent is valid in such cases.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".