Bibliographic record
Abstract
plans for financial services legislative renewal, the rest of us ought to spare a thought for the government’s languishing “productivity agenda. “ Could the white paper set the stage for action on productivity in Finance Minister Jim Flaherty’s own bailiwick? It should, because smartly delivered financial intermediation is extraordinarily important to our economy and a vital policy goal. Growing productivity, or the value of output per hour of work, is key to rising wages and living standards; hence the past decade’s languid productivity performance partly explains slow growth in Canadians ’ incomes and household spending.1 While Canada’s persistently strong labour market is good news, growth in real output per hour has been slow compared to other developed countries, with our performance over the years 2000 to 2004 putting us at 24th of 29 OECD countries. Both total output and Canadian incomes have increased at a resolutely middling pace (Table 1).2 Against that lackluster backdrop, the 2005 figures provide a few surprises, including an unpleasant one from the financial services sector. Some sectors are looking strong on the productivity front — manufacturing recorded its sixth consecutive quarter of labour productivity growth running above 4 percent. However, the broad financial services sector showed a surprisingly weak result, with its fifth consecutive quarter of declining labour productivity (Figure 1).3 If financial sector productivity had kept pace with the rest of the economy in 2005, the headline growth rate would have been about 0.2 percentage points higher on e-briefC.D. Howe InstituteInstitut C.D. Howe
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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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".