Bibliographic record
Abstract
Don Drummond1 Queen’s University ONE OF MY RESPONSIBILITIES WHEN I joined Finance Canada in 1977 was to adapt the Bank of Canada’s RDX model to do simulations of policy options. Ian Stewart had been one of the principals in the path-breaking work at the Bank to develop this early version of an economy-wide econometric model. His name loomed large in the model documentation I studied. In a case of “isn’t it a small world,” other principals included Fred Gorbet, co-editor of this volume, and John Helliwell, another contributor. I would later have the opportunity to know Ian Stewart in person when he became Deputy Minister of Finance in the 1980s. He instilled an analytical discipline in the Department’s work that combined rigour in theory and quantitative methods. A good deal of this policy work was put into the public domain where it could be scrutinized.
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 | 0.062 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.024 | 0.034 |
| Scholarly communication | 0.032 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.015 | 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".