The Negotiator as Professional: Understanding the Competing Interests of a Representative Negotiator
Bibliographic record
Abstract
This article is about lawyers as negotiators, and in particular, it is about identifying and understanding the influential and potentially competing interests that are - or at least should be - in the minds of lawyers (and potentially other third party representatives) during the overall negotiation process. While there continues to be an increasing amount of literature on the mechanics and strategies of negotiation, the underlying interests that are typically at stake in representative negotiations from the perspective of representatives - particularly negotiations involving lawyers - have not been adequately studied. Current accounts of the representative negotiator do not paint a full picture of what is typically going on inside the representative's mind, and as such, provide an impoverished view of his or her role, both in terms of its responsibilities and its potential opportunities. To address these deficiencies, this article advances an alternative, expansive model of the representative negotiator: the negotiator-as-professional model. It is a model that sees the role of the representative negotiator as being defined by at least four sets of interests: client interests, a broad understanding of the representative's self-interests (that may include, but are not limited to, interests vis-a-vis the representative negotiator's bargaining opposite), ethical interests and the public's interests.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".