Bibliographic record
Abstract
This manuscript provides an empirical documentation of the existence and magnitude of heterogeneous bid shading in the discriminatory auction. Unlike existing empirical work that uses aggregate data, this manuscript makes use of a unique data set collected at the individual bidder level containing bidding behaviour and detailed cost information covering a natural experiment in which the format of the auction switches from uniform to discriminatory pricing. Preliminary regression results indicate that bid prices in the discriminatory auction are $1, 813 lower than bid prices in the uniform auction. In other words, the magnitude of bid shading in the discriminatory price auction is on average 6.3% of bidders’ willingness to pay. Capacity utilization, housing type and milking system are bidder-specific characteristics identified as determinants in explaining heterogeneity in bid shading across bidders. In terms of allocative efficiency, the uniform auction achieves a higher average efficiency of 91% compared to an average efficiency of 74% reached by the discriminatory auction. The majority of the inefficiency of the discriminatory auction is attributed to the use of bid-spreading strategies while the remaining portion of inefficiency is due to heterogeneous bid shading across bidders. All auction inefficiency identified in the uniform auction is attributed to the use of bid-spreading strategies.
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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 | 0.017 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".