Bibliographic record
Abstract
In this introduction, Andrew Sharpe provides the context for the Symposium on Future Productivity Growth in Canada, a panel organized by the CSLS at the Canadian Economics Association Meetings, June 2003 at Carleton University. He provides a context for the presentations by highlighting certain issues related to the topic not directly addressed in the symposium. Total hours worked is a more accurate measure of labour input than persons employed, so that when data on hours are available, an hours-based aggregate labour productivity measure is preferable to a worker-based measure. An important reason for choosing the total economy as the appropriate measure of aggregate labour productivity is that the potential for real income gains is determined by economy-wide aggregate productivity increases, but the business sector accounts for only around three quarters of total economy output. Small increases in productivity over long time periods and the real income gains these generate have extremely favourable consequences for the affordability of social programs. Finally, while the contributors to the symposium reach a consensus of 2 per cent per year labour productivity growth in Canada over the next 25 years, there is no such consensus in the broader economics profession, with some forecasters alternatively predicting labour productivity growth as low as 1.6 per cent per year.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 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".