Results of five years Grit-Rouge rule in Canada : the steel rail purchases.
Bibliographic record
Abstract
No subject has been more discussed sinee Mr. Mackenzie came into office than his unfortunate purchase of steU rails.A simple record of the facte in this cate is all that is necessary to show, first, that th© purchase was a most unwise one ; next, that it was without the authority of Parliament; and, lust, that it was open to the pave suspicion of having been prompted by a spirit of nepotism.Ir will bo remembered that Mr. Mackenzie's first proposal in relation to the Pacific Railway was to utilize the water stretches All the railway, thereto e, to be built bj him was about 45 miles from Lake Supe- rior to Shebandowan.and about iOO miles from the northwest angle to Fort Garry and the Pembina, branch of about 7(! miles, making altogether a li tie over 200 miles of railway, which ho had the i.nme diatejntcntion of buildinir.In the fall of 1874, he advertised for tenders for some rails, and the first buspicious circumstance connected with the matter was the man ner in which these advertisements were inserted.Jt will be admitted that it was a matter of the greatest possible import ance that the fullest publicity should b e given to any invitation lor tenders of this description.The manufacturers of steel rails were in England.Their agents in this country must of necessity communi- cate with them, and unless, therelore there was time lor that communica.ion'the trade at large must necessarily be put to a very great disadvantage.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".