Role of the Public Sector in Agricultural Coopetition: Place-based Marketing in Post-WTO Rural Taiwan
Bibliographic record
Abstract
Since the Uruguay round, the trend of liberalization in agricultural trade has been unstoppable. Although countries were eager to become members of the World Trade Organization (WTO) after the 1990s, agricultural production, trade and consumption patterns have also been undergoing rapid changes both in developed and developing regions. How have governments in different countries reacted to these changing realities and challenges? In addition to practicing conventional protectionism, such as farm subsidies and tariff wars to sustain the domestic agricultural industry, what else can the public sector do to reform agriculture? This research studied four public-private partnership cases in southern Taiwan to demonstrate an alternative governmental response to the changing agricultural trade, where the public sector induces competing farmers to cooperate. Specifically, the government encouraged the farmers in rural communities to engage in a so-called "state-led coopetition" strategy to promote place-based marketing and collectively create a competitive advantage in the post-WTO era. The research focuses on why and how competing farmers cooperate and the impact of state intervention on coopetition. In terms of research contribution, this study first addresses the theoretical and empirical deficiencies in discussing the role of the public sector in coopetition strategy. Second, after a careful examination of the motivation, implementation and outcome in the four state-led coopetition cases, four major findings are identified to advance coopetition theory building. These findings are the following: 1) crises are focusing events that induce coopetition behavior; 2) competing firms in state-led coopetition cooperate and compete differently than in a typical business environment; 3) not all coopetition that is led by the public sector is unintentional; and 4) state-led coopetition generates extra public value.
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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 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".