Bibliographic record
Abstract
Our society perennially seeks to multiply its connectivity in the name of greater efficiency. Over the past few years, several devices that had previously been quite basic have been made ‘‘smarter” in order to facilitate a consumer’s life. A recent study highlights that some of the most common reasons for using ‘‘smart” objects are home automation and remote control. Thus, convenience is driving companies, particularly appliance makers, to connect their devices to the internet in order to make them ‘‘smart”. These range from intelligent thermostats, smart fridges, connected pacemakers, smart watches and personal assistants (PAs) such as Alexa, Siri or Cortana, which exist within most devices of their parent companies. While innovation is the engine of the future, one has to wonder whether these recent advancements bring us too close to the Dickian and Orwellian futures we have been warned about for decades. The fear was never that we would get too advanced, since technological advancements are usually inherently positive, but rather that our penchant for an ever-present connection would strip us of our intimacy. The paper explores the privacy concerns that emerge from connected objects. More specifically, it examines how these objects fit within the framework of Quebec’s privacy legislation, as well as Canada’s federal privacy legislation. It also seeks to highlight the current flaws in the application of this framework to connected objects.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".