Futures of Data Ownership: Defining Data Policies in Canadian Context
Bibliographic record
Abstract
The importance of data is increasing along with its inflation in our world today. In today's world, data is becoming the primary source for innovation, knowledge, insight, and a competitive and financial advantage in the race of information procurement. This interest in acquiring and exploiting data and the current concerns regarding the privacy and security of information raises the question of who should own the data and how policies can preserve data ownership. There is a growing awareness that companies benefit disproportionately from collecting and selling personal information, driving the desire for greater individual control of personal data. As technology progresses exponentially, there is a dire need to regulate Tech organizations. \n \nWith the increasing use of personal data by tech companies, data privacy and ownership concerns have become more significant in today's society. Although governments worldwide have introduced privacy regulations to protect citizens' data, there is still a need for policies and legislation that safeguard citizens' rights, allow consumers to control their data, and implement strict measures in case of data breaches or violation of data rights. \n \nThe research project "Futures of Data Ownership - Informing Data Policies in Canadian Context" aims to explore emerging technological shifts and promote ethical use and data protection by developing data policies that consider the Canadian context. The research will employ primary and secondary research methods, including horizon scanning, semi-structured interviews, and a literature review, to inform policy and strategy development. In conclusion, the research project informs potential policies and legislation that regulate tech organizations and protect data ownership, ensuring a secure and trustworthy digital future for all.
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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.037 | 0.026 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".