Bibliographic record
Abstract
This discussion is in two parts because there are two stories. The fi rst looks at the experience with infl ation from the early 1970s up to 1987. The second examines what happened in the years after, focusing mainly on what hap-pened during my seven- year term as central bank governor, from early 1987 to early 1994. I should add that I was at the Bank of Canada from 1973, and for a few years before that was working on Canada for the International Monetary Fund (IMF). So this account refl ects considerable direct knowl-edge of, and substantial involvement in, what happened throughout this period and the reasons why. It is also, of course, unofficial. The Period to 1987 For many years up until 1987 the Bank of Canada, and from time to time the Federal government, were preoccupied with struggling over, and pushing back, an escalation of infl ation. This escalation stemmed from bad luck, compounded by policy misadventures. The bad luck was twofold: fi rst, in the late 1960s being tied under Bretton Woods to the US economy as infl ation-
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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.037 | 0.008 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 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".