Ontario elections 1883; facts for the people, should be read by every elector.
Bibliographic record
Abstract
One of the great advantages claimed for the Crooks Act was that it would largely reduce the number of licenses, and by keeping thero down, advance the cause of temperance and sobriety.How has it ful- filled that anticipation in its practical working ?In lb'7-i the greatest number of licenses ever issued in the Province was reached, being 6,185 ; but the revival Of the temperance reform which set in about that- time reduced them in 1875 to 5,818 (License Report 1881-2, p. 15), or a reduction of 3G7 in one year, and doubtless had there been i;o change in the law the growing temperance sentiment would have continued to' effect a reduction.But the statutory reduction which the \ cessur^j of- public opinion compelled the Government to make in the Crooks Act effected a reduction in 187G to 3,038, and next year (when those statut- ory reductions came into full force), to 3,G76.Since that time, however, the Commissioners appointed by the Government, instead of keeping down the number of licenses, have gone on steadily increasing them year by year.Here are the figures as taken from page 15 of the last License
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.139 | 0.039 |
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".