Secrecy, Security and Digital Literacy in an Era of Meta-Data: Why the Canadian Westminster Model Falls Short
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this article is to undertake a critical assessment of the governance of Canada's national security apparatus and, more specifically, the growing digitization and data-driven dimensions of such an apparatus. Over the previous decade, since 9/11, the advent of electronic government (e-government) and its emphasis on horizontality and interoperability became intertwined with the security apparatus of the public sector: the recent Snowden affair in the US has once again brought such discussions to the forefront. It is within such a context that the rise of ‘big data’ (or meta-data) as an identifiable term denotes a confluence of forces and contradictory tensions between openness and secrecy for the public sector both operationally and democratically. We examine Canada's Westminster insularity in this regard, how Canadian reforms meant to augment oversight and review capacities of security agencies have been stunted in recent years, and why such stalled actions matter to the privacy and safety of Canadian citizens. Conversely, a case for more openness and governance innovation is put forth premised on two main and inter-related directions: more political oversight and public dialogue on the one hand, and a greater emphasis on privacy as a responsibility on the other hand. Together these directions emphasize a more activist and participative civil culture that is central to ensuring societal resilience in an increasingly virtual and complex security environment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it