Social Networking and Political Activism: The Great Canadian Copyright Fight - Over or Not?
Bibliographic record
Abstract
In 2007 and 2008 there was a hard fought campaign against proposed changes to the Canadian Copyright Act, changes which would have privileged producers and owners of copyright over users. Particularly notable about the campaign was the following: 1) It was organized primarily by means of Web 2.0 tools, in particular, Facebook; 2) It was about copyright normally a subject of little interest to the public. Why the uproar over copyright? Why were Web 2.0 tools the preferred means of political activism? How were these tools used? With what success? What are the limitations of Web 2.0 tools in online campaigns? The fall 2008 federal election prevented passage of the legislation but the Harper government has promised to revive it this spring. Will it? This presentation examines these questions
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.030 | 0.023 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".