Moose Pastures and Mergers: The Ontario Securities Commission and the Regulation of Share Markets in Canada, 1940-1980
Bibliographic record
Abstract
Long before the spectacular collapse of Bre-X in 1997, the Canadian capital markets had their share of swindlers and crooks. In the boom times after Second World War, hard-sell speculative mining ventures, pushing what often amounted to a few acres of moose pasture, riddled over-the-counter markets and the TSE. It was in this context that the Ontario Securities Commission developed into Canada's leading securities regulator. Following the war, the OSC concerned itself primarily with fraudsters and attempts to reign in Toronto's boiler rooms, but by the mid-sixties increasingly sophisticated markets and a series of scandals culminating in the Windfall affair resulted in a rewriting of the Securities Act and a widening of the OSC's investor protection mandate. The seventies tested the Commission's new powers as increased corporate merger activity brought the phrase .insider-trading. into the popular lexicon. Surprisingly, considering that capital markets have such a profound impact on Canada's well-being, this is the first thorough study of the their post-war evolution and regulation. Moose Pastures and Mergers takes off where the author's acclaimed previous work, Blue Skies and Boiler Rooms: Buying and Selling Securities in Canada, 1870 - 1940, left off. With an ear for a good story - seedy personalities, bunglers and guileless victims abound - and a scholar's rigour, Armstrong has met the protean beast of share markets head on and revealed its shape for the timid or the merely baffled. Essential reading for business journalists, securities lawyers, academics, and interested investors. Winner of the J.J. Talman Award presented by the Ontario Historical Society
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".