The new social media paradox: A symbol of self-determination or a boon for big brother?
Bibliographic record
Abstract
In the past ten years or so, mobile phone and Internet technologies have been instrumental in nearly every instance where people have gathered to demand political reform. With the help of 'new social media' applications, like Facebook and Twitter, Internet and mobile phone users can conduct realtime exchanges with millions of people across the globe. Following the Introduction, this Article begins, in Part I, with a discussion of how these tools were used by protesters around the world in 2011. Part II discusses how the same tools were used by governments, both democratic and authoritarian, to respond to the violence and mayhem during that year. In Part III, I turn to a discussion of the relevant policy concerns, first in the American, then the Canadian, legal contexts. It is significant that Canada is the first country to complete an extensive investigation into Facebook's privacy practices. As a result, Facebook users across the world now enjoy stronger privacy protections for their personal information, in terms of how it is collected, used and disclosed. In conclusion, I note that this case has important implications for other online social networking sites, even those based in other countries, which are collecting and using the personal information of Canadians in a way that does not comport with Canadian privacy laws.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".