Bibliographic record
Abstract
The paper by Crawford and Harrison focusses on an issue at the forefront of the current debate over inflation targeting and price stability: the extent of nominal wage rigidities in Canada’s labour market. I have been asked to provide a technical discussion of the paper. Most of my comments thus focus on the data and techniques used by the authors to address this issue. I commend the authors on their use of multiple data sources. Unfortunately, there is no one “best ” source for answering questions about nominal wage rigidities. The authors try to put together an overall picture by bringing together evidence from several data sets. They find that nominal wage rigidities are present in union wage settlements data. However, alternative data sources indicate that more flexibility exists in wage-setting and compensation practices in the Canadian labour market than the large union contract data would lead one to think. Unfortunately, these data are not ideal either, since they cover shorter time periods and selective, smaller samples. More work needs to be done, therefore, before any broad conclu-sions can be reached. The authors ’ data are derived mostly from union wage settlements data covering contracts at unionized firms with more than 500 workers, and providing information only on changes in base pay. The evidence from this main source can be divided into two categories: supportive and not supportive of the presence of nominal wage rigidities in Canada. The evidence consistent with a strong presence of wage rigidities is threefold: (1) the fraction of wage freezes increases during the low-inflation period of 1992-96, (2) the percentage of rollbacks in the sample is small, and (3) the
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.299 | 0.115 |
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".