Bibliographic record
Abstract
I would like to thank the FCAR and the SSHRC for generous financial support, and André Kurmann, Louis Phaneuf, and seminar participants at the Université du Québec à Montréal for helpful discussions. The usual caveats apply. Résumé: Les modèles des microfondements des rigidités nominales montrent qu’en présence de rigidités réelles, les firmes ont une incitation très forte à ajuster leurs prix même si les autres firmes ne le font pas: la rigidité des prix n’est pas un équilibre de Nash à moins que le coût fixe d’ajuster les prix soit trop élevé pour être plausible. Nous montrons que la rigidité des salaires nominaux peut être un équilibre de Nash même sans rigidités réelles et lorsque le coût fixe d’ajuster le salaire nominal est relativement faible. La taille du coût d’ajustement nécessaire pour supporter la rigidité des salaires nominaux décroît au fur et à mesure que l’élasticité de l’offre de travail augmente, mais elle reste très faible pour des valeurs empiriquement plausibles de cette élasticité. La taille nécessaire du coût d’ajustement n’est pas sensible au degré de substituabilité entre les types de travail dans la fonction de production. Abstract: Models of the microfoundations of nominal price rigidities show that in the absence of real rigidities, individual firms have strong incentives to adjust prices even
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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".