Precedents in Negotiated Decisions: Korea–Australia Free Trade Agreement Negotiations
Bibliographic record
Abstract
Abstract Initial random acts can be replicated and evolve into precedents, but precedents can also be built with strategic intent. Regardless of their origin, strategically applying a particular precedent or effectively refuting the relevance of a precedent can help a negotiator control decisions and achieve interdependent goals. The purposeful use of precedents has received little attention in the negotiation literature, even though using precedents can be a powerful negotiating tactic. In this study, we examine how past decisions became precedents that helped establish the Korea–Australia Free Trade Agreement of 2014 (KAFTA). We further consider how precedents established through KAFTA later influenced trade negotiations with Canada, China, India, and Japan. Following an extensive literature review and field research, we developed a two-dimensional matrix (precedent ownership and negotiator goals) to help guide negotiators both offensively (what I want from you) and defensively (what I don't want to give you). We conclude by proposing research to enhance our understanding of temporal issues in negotiation. No previous study within the negotiation literature has examined precedents empirically.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".