Bibliographic record
Abstract
◦ This case study illustrates the difficulties a cartel can face in getting out of a price war and identifies some of the obstacles to doing so related to market circumstances. It does so by closely examining a price war that upset collusion in Québec City in 2000. ◦ The episode started when an independent retailer chose to defect from the collusive agreement by lowering its price so as to increase sales volume and benefit from a price-support clause it had with an upstream supplier. This act triggered a price war that caused margins to go from five cents per liter to nearly zero and which lasted almost a full year. ◦ Using daily station-level price data, the case study shows the price war lasted so long because it was very costly for any firm to take the lead to return to collusive prices. The root of the problem was the high price elasticity of firm demand. Raising price by only two cents per liter above neighboring prices could result in a 36 percent loss of volume. Thus, a firm raising the price to get out of the price war would experience a significant drop in sales as it waited for other firms to match its increase. This deterrent to raising price was compounded by one of the leading firms having a low-price guarantee which tied its price to the lowest price in the market.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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 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".