Progress on the Mystery of Productivity: A Review Article on the OECD Report 'The Future of Productivity'
Bibliographic record
Abstract
For decades the OECD has been a leader in the research of productivity and the development of policy ideas to strengthen it. Yet many countries, including Canada, that have implemented the OECD paradigm reasonably faithfully have not experienced strong productivity. Indeed, many OECD members, again including Canada, have experienced a decline in multi-factor productivity over the past 15 years. The record makes it fair to conclude that the OECD has not cracked the mysteries of productivity. So when the organization releases a new study under the bold title of The Future of Productivity certain questions are natural. Does the OECD have new and different perspectives to add? Will these perspectives lead to policy ideas that will succeed in raising productivity growth among member countries? The answer to the first question is a resounding yes. The OECD flags, quite appropriately, that the research needs to go beyond the so-called policy fundamentals and examine the behavior or firms, particularly why some firms so badly lag others in productivity. The OECD also now places much more emphasis on how human talent is allocated to jobs. This also seems promising. Whether such new directions of research will finally crack the mystery of productivity remains to be seen. But the OECD research program is heading in the right direction and warrants close attention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".