From Syphilis to Autism, How the Anti-Vaccination Movement of Today is an Echo of the Past
Bibliographic record
Abstract
Introduction: The anti-vaccination movement has led to decreased vaccination rates and increased vulnerability to vaccine-preventable diseases in the general population. In order to better understand the anti-vaccination movement of today, the anti-vaccination movement that emerged in the 19th century is examined and measured against the one observed in the 20th century. Discussion: Though the population of the 19th and 20th centuries differ in many regards and our knowledge of vaccine and immune mechanisms are far greater; the anti-vaccination movement seen today stands on the same pillars as that of the 1800s with the sentiment of fear at its core. Though the façade of these pillars has been altered to suit the world today, both movements exploited the influence of prominent public figures, maintained false associations with dire vaccine consequences and emphasized these through the use of visual media, repetition and personal narratives. The persistence of the anti-vaccination movement lies largely in the use of personal stories which are more impactful and memorable then the statistical characteristics of scientific study. Conclusion: The pro-vaccination movement must respond to the tactics used by the anti-vaccination movement and create accessible, understandable and equally impactful communication strategies in order to prevent the spread of misinformation and counter the efforts of the current anti-vaccination movement. Relevance: Vaccine hesitancy was listed amongst the top 10 global health threats in 2019 by the World Health Organization. In order to shift the negative rhetoric surrounding vaccines, the anti-vaccination movement of today and its historic roots need to be understood.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".