Bibliographic record
Abstract
Public policy considerations intersect with virtually everything the financial services sector does, yet governments make many public policy decisions without specific data on the Canadian marketplace. In Financial Services and Public Policy, contributors address this shortcoming by considering a wide range of concerns, including lending to small businesses, the role of microcredit and raising venture capital, the impact of state guarantees of pension plans, the structure and performance of credit unions, and maintaining competition after bank mergers. The financial services sector drives the Canadian economy. It is the banker, lender, broker, and insurer of millions of Canadians at every stage of their lives. It provides corporate finance, shapes the growth of small business, and assists individual Canadians to achieve their financial goals. It is a major employer all across the country. Canadian financial institutions are also active globally and are influenced by international as well as domestic policy issues and decisions. Financial Services and Public Policy lays the foundation for today's public policy debates and decisions that will shape Canada's financial services sector into the twenty-first century. Contributors include Nick Bontis (McMaster University); David A. Brown (Ontario Securities Commission); John Chant (University of British Columbia); Douglas J. Cumming (University of Alberta and University of New South Wales); David Dodge (Bank of Canada); A. Ellen Farrell (St. Mary's University); Klaus P. Fischer (Laval University and CIRPEE); Mario Fortin (Universite de Sherbrooke); Fred Gorbet (York University); Jean-Pierre Gueyie (Universite de Quebec a Montreal); George Haines (Carleton University); Tessa Hebb (University of Oxford); Lewis D. Johnson (Queen's University); Mary Kelly (Wilfrid Laurier University); Anne Kleffner (University of Calgary); Andre Leclerc (Universite de Moncton); Jeffrey G. MacIntosh (University of Toronto); Harold MacKay (Macpherson Leslie and Tyerman LLP); Judith Madill (Carleton University); Nadia Massoud (University of Alberta); Edwin H. Neave (Queen's University); Norma L. Nielson (University of Calgary); Marie-Helene Noiseux (Universite de Quebec a Montreal); Tony Porter (McMaster University); Iain Ramsay (York University); Allan L. Riding (Carleton University); Christopher Waddell (Carleton University); and Toni Williams (York University).
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.031 | 0.014 |
| Insufficient payload (model declined to judge) | 0.069 | 0.011 |
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".