2016 06 28 Fair Vote Guelph Vote For Canada
Bibliographic record
Abstract
Fair Vote Guelph presents Vote For Canada Presenters: Steve Dyck and Byron Weber Becker; Moderator: John Lawson Steve Dyck and Byron Weber Becker provide information about voting systems (First-Past-The-Post, Multi-Member Proportional, Mixed-Member Proportional), and take questions from the audience. Additional information is provided by guest speakers. Recorded by Bob Jonkman and Laurel L. Russwurm. Attribution should be "Creative Commons Attribution-ShareAlike 4.0 CC BY-SA Fair Vote Guelph, Bob Jonkman and Laurel L. Russwurm"
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Recording of a Fair Vote Guelph public meeting on electoral systems; civic advocacy content with no research-practice object.
This recording concerns electoral voting systems and political advocacy, not research practice.
Recording of an electoral-reform public event; political advocacy content, not metaresearch.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.364 | 0.147 |
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".