Bibliographic record
Abstract
Puzzles intrigue and motivate researchers and focus research effort, and the productivity area is fortunate in having many unresolved issues. In the second article, Andrew Sharpe of the Centre for the Study of Living Standards puts forward and briefly discusses what he sees as the ten most important productivity puzzles facing researchers in Canada and in other countries. In terms of the international puzzles, he considers the causes of the post-1973 productivity slowdown that affected virtually all industrial countries the grand daddy. He also identifies the post-2000 productivity growth acceleration in the United States, labour productivity levels in a number of European countries that exceed U.S. levels, and the absence of a post-1995 productivity growth acceleration in Europe as developments that are currently not well understood. In terms of productivity puzzles related to Canada, he identifies the considerable difference in labour productivity growth in the non-business sector between Canada and the United States as a topic meriting investigation. He also sees the Canada-U.S. productivity gap and Canada’s relatively low machinery and equipment capital intensity as puzzles meriting in-depth research.
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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".