Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper reviews the theory related to the estimation of damages arising from pricefixing. Our primary objective is to provide an overview of the major issues that arise when estimating these types of damages and we suggest how economists might reasonably proceed when undertaking to provide such estimates. We describe and critique the leading approaches to damage estimation in price-fixing cases with a particular emphasis on reduced-form econometric estimation of the price that would have obtained in the market “but for” the price-fixing. We also consider complications introduced for the estimation of both the magnitude and the distribution of the damages in cases in which the first buyer (a “direct purchaser”) of a price-fixed product resells it or incorporates it into a product which is then sold to (“indirect”) purchasers further downstream. * The authors are also both Senior Consultants with the Delta Economics Group Inc. They are grateful to the Phelps Centre for the Study of Government and Business in the Sauder School of Business at UBC and to the Social Sciences and Humanities Research Council of Canada for financial support; and to Ann-Britt Everett and Jennifer Ng for excellent research assistance. As part of their work on cases involving damage assessment, they have also benefited significantly from discussions and communications with John Beyer, J. J. Camp, John Conner, Joe Fiorante, David Jones and Charles Wright.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it