Bibliographic record
Abstract
In 1982, the introduction of compulsory vaccination legislation pushed parent activists in Canada to form the Committee Against Compulsory Vaccination (CACV). Parents felt moved to act on behalf of their children, who they believed were harmed by the Diphtheria Tetanus Pertussis (DTP) vaccine. Canadian parent activists found themselves part of a wider crisis of trust in the DTP vaccine. The vaccine had been anecdotally linked to brain damage, and there was uncertainty about the scale or existence of harm. By the time parents in Canada organized litigation against vaccine manufacturers, however, the uncertainty was resolved. Regulatory structures, put in place because of parent activism, generated research which undermined the extent of their claims of harm. Throughout the 1990s activists leveraged alternative media and personal networks to reach individual parents. Soon, activists across the English-speaking world shared newsletters and strategies with each other. Activists produced diligently cited material to argue for their cause. With the rise of the World Wide Web, the parent activist network moved online. Links worked like footnotes, guiding readers through hand-picked sources that seemed to show proof of harm. The scale of links and the decentralized network presented a cohesive thesis of widespread harm. But the internet also moved attention toward American groups and media personalities, and Canadian parents who had participated in the network moved to the margins. Networks of Trust is a study of activism that blends digital and traditional archival methods to enhance historical argumentation. I suggest a methodological pipeline for reconstructing and analyzing early-web networks from archival crawls. By placing Canadian parents within a wider digital ecosystem, the dissertation clarifies how activist groups persisted, and reshaped public debates about vaccines.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".