Bibliographic record
Abstract
In this chapter, Andrew Sharpe provides a comprehensive non-technical introduction to the productivity issue, including discussion of productivity concepts, measurement issues, trends and prospects. He begins by noting that productivity is the relationship between the output of goods and services and the inputs of resources, both human and non-human used in their production. The measurement of productivity is fraught with conceptual and empirical issues, meaning that there can be a significant margin of error associated with productivity growth rates, even at the aggregate level. Sharpe identifies two particularly important measurement problems, namely the estimation of real output in the non-market sector where output is not measured independently of inputs and the estimation of price indices (which are needed to calculate real output) for products where quality has improved significantly or for new products (e.g. computers). According to Sharpe, the most important productivity trends that the general public should be aware of are: the post-1973 productivity slowdown; the postwar convergence in OECD productivity levels toward the US level; the post-1995 acceleration in labour productivity growth in the United States; the decline in Canada's relative international productivity ranking; and the widening of the Canada-US manufacturing productivity gap.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".