Bibliographic record
Abstract
Vaccines have been described as one of the greatest achievements in combating infectious diseases and promoting human health. However, not everyone shares this view. For some, childhood vaccinations are controversial, leading to individuals choosing to not vaccinate their children. To examine issues surrounding childhood vaccination, the Discourse, Science, Publics (DSP) Lab at University of Guelph recently hosted the Ontario Vaccine Deliberation in Waterloo, Ontario on October 14, 15, 28 and 29, 2017. Twenty-five randomly selected adults from across Ontario came together to share their views on childhood vaccination in a public deliberation event. Prior to the event, participants received the Ontario Vaccine Deliberation Information Booklet. During the event, participants heard from several experts and key stakeholders with diverse views on vaccination and related issues and worked together in small and large-group formats over the 4-days to identify and discuss important issues relevant to childhood vaccinations. As a collective group, the participants developed policy recommendations that reflect the diverse views of the group.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".