Bibliographic record
Abstract
According to the Statistics Canada report from 2019, when it comes to the amount of time spent online, Canada beats out every other country in the world. This has likely been amplified due to the stay-at-home order caused by the COVID-19 crisis, hence why the new Bill C-11 will strengthen the current policies defending Canadians from corporate digital overstep. Alexa, Please: Babysit My Child will explore, analyze, and evaluate Amazon's neuro-capitalistic technologies, specifically pertaining to the technologies made for child-use. Neuro-capitalism is dangerous as it speaks to controlling the mind through the current hyper-technological society. Jurisdictional complexity surrounding A.I. and cybersecurity can be mitigated by government-funded education. Therefore, my research explores the question: From a neuro-capitalistic & digital-colonial standpoint, to what extent are Amazon's child-targeted technologies' (such as Kindle 4 Kids) consistent with the privacy policies of the new, proposed Bill C-11? This policy analysis will consist of three sections—first, an analysis of Amazon's Kindle 4 Kids Terms and Conditions (Site 1). Second, an evaluation of Bill C-11’s ability to protect children from the pernicious aspects of neuro-capitalism (Site 2). Lastly, a compare and contrast section of the two entities, ending with a discussion of the findings. Particularly during the COVID-19 crisis, we must be sure that the Government of Canada is doing everything in their power to aid the youth of the country that spends the most time online and the most time with their babysitter: Alexa.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.282 | 0.089 |
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".