Does acquisition of a cooperative generate profits for the buyer? The Dairyworld case
Bibliographic record
Abstract
Abstract This article examines the takeover of a cooperative (Dairyworld) by an investor‐owned firm (Saputo) that was not previously present in the industry, determines if this takeover generates greater returns for the investor‐owned firms (IOF), and on the basis of this evidence makes some inferences about the behavior and performance of cooperatives and IOFs. The empirical evidence strongly supports the conclusion that Saputo's stock price rose with its takeover announcement. This outcome is consistent with a number of explanations, including that Saputo was unaffected by hubris, a factor often suggested as the reason that many firms overbid when they undertake acquisitions. Dairyworld's poor liquidity and capital shortage problems, as well as a limited number of suitors, may have weakened its bargaining position in its dealings with Saputo. The observed increase in Saputo's stock price is also consistent with the possibility that, by taking over a cooperative, Saputo was able to decrease competition and thus increase its profits. A fruitful area for future research would be a rigorous theoretical and empirical determination of the impact that these various factors have on acquisition profitability. Such analysis is required before inferences about the behavior and performance of cooperatives and IOFs can be fully answered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".