Bibliographic record
Abstract
Over the past decade, questions over the impact of new information technologies on productivity growth trends have played an important role in the formulation of monetary policy, particularly in the United States and Canada. However, formal testing of whether the trend growth rate of aggregate productivity has changed significantly is rare, and the best work done to date appears to reach conflicting conclusions. The recent literature is also silent about our power to detect such changes; that is, the extent of the tradeoff between the size and persistence of a structural change in productivity and probability that the policy analyst might remain ignorant of its existence. This paper examines the existing evidence for a shift in aggregate trend productivity growth and attempts to assess its reliablity as a basis for policy making. First, it formally tests for recent changes in productivity growth trends using a new test for instability at the end of samples. Second, it uses a new real-time data set on aggregate Canadian productivity growth to assess the extent to which data revision complicates inference about trend growth rates. Third, simulations are used to quantify the degree to which the lag in detecting breaks may be affected by the size of the break.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".