The political position in Canada.
Bibliographic record
Abstract
very credulous follower in this House.When the hon.gentleman vent- ured to say that the leader of the Government and the Government had redeemed all their promises, I fear that, while he shows a disposition to give unbounded credit to his leader, he also shows that he has not given that careful attention to the subject which alone would enable him to speak with authority.Instead of such an extravagant claim being well founded, the hon.gentleman will find that, when challenged to put his finger upon a single promise made to the electorate that has been fulfilled by the Government and its leader, he will be unable to do so.(Hear !Hear !)While speaking of the pleasure with which I have, listened to these hon.gentlemen, I must not forget the very kind and complimentary references that were made to myself by the seconder of this Address, I should be glad to think that I was entitled to even half the commendation which he was good enough to bestow upon me.I will, however, endeavour, as we, become better acquainted, to convince the hon.gentleman that, whetn^p , right or wrong, in discharging the high and important duties that, deyolve upon me, I seek, at all times, to take such a course as will coi^inee hum that, though we may not see eye to eye, I am moved only by.what I believe I owe to the House and to the country.(Applause).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".