Putting Together the Pieces of the Productivity Puzzle: Review Article of Productivity Perspectives and Productivity and the Pandemic
Bibliographic record
Abstract
The productivity puzzle in the UK may have taken a turn with the arrival of the COVID-19 crisis although we do not know at this point whether it will be for the better or the worse. The two edited volumes discussed in this review article are distinguished by the first being produced just before the pandemic, and the second in the midst of it. Together, the volumes address a broad range of economic, social and policy issues related to the productivity puzzle in the UK, with a strong focus on organizations, management, entrepreneurship, innovation and skills. In addition to examining productivity growth at the firm level, the volumes also analyze differences in productivity and income between firms, workers and regions. There is also a strong plea for a system-based approach to policy making for productivity. On the whole, the contributors take a cautious approach on how much the pandemic will change productivity performance in the medium-term, but they argue strongly in favour of active policy intervention to mitigate the damagea rising from the pandemic and create better conditions for a sustained productivity revival.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".