Priorities and Directions for Future Productivity Research: Health Care, Intangible Capital and High-tech
Bibliographic record
Abstract
This article identifies health care, intangible capital, and the high-tech sector as priority areas for productivity research. In terms of health care, it highlights the importance of getting prices right and the key role that a satellite account for health care can play for productivity measurement. Regarding intangible capital, it stresses the importance of developing better price deflators for investment in tangible capital as well as better depreciation rates. Finally, it notes that because of the rapidly changing nature of the high-tech sector measurement issues remain a priority. I AM VERY PLEASED TO HAVE an opportunity to participate in this panel discussion. Each panel-ist was asked to discuss three agenda items for future research on productivity. It is challeng-ing to be limited to just three items as many topics are worthy of further research. That said, I will focus on three items in the area of
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".