Beyond the Deal: Next Generation Negotiation Skills Introduction to Special Issue
Bibliographic record
Abstract
At a recent alumni function, the conversation turned to what makes for an effective negotiator.One of the alumni in the group challenged me for being ''soft''-he argued that real negotiation was about getting the best economic deal for yourself or your organization.He went on to say that a negotiator should be concerned with ensuring that no value was left in the table and this turned conversation, briefly, to factors such as anchoring and the fixed-pie bias, both of which can prevent negotiators from getting a good deal.One of my students was in the same group and interrupted to say ''I got the impression from Mara that if you can't get a good economic deal and maintain the relationship, then you are a bit of a wimp.''For me, the comments from the two alumni reflect the ''before'' and ''after'' in how we think about negotiating.For many years, our research and our teaching focused on the deal.Working with the concepts of value claiming and value creation, we taught our students the competitive and collaborative tactics that served these goals.Although we recognized that the underlying relationship was important, relationship issues were not addressed directly.In recent years, there has been a shift in how we think about negotiation.As we have solved the ''simple'' problem of crafting good deals, attention has turned to other aspects of the negotiation.Although we have long recognized that negotiations present individuals with a complex, multilayered process, until recently our focus has been only one layer of this process.Yet, in order to craft a deal, negotiators must manage at least three distinct layers: the substantive aspects of negotiation, i.e., creating and claiming value; the social processes that underpin and shape negotiators' ability to craft a deal; and the increasingly complex environment in which deals are made.To manage each of
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.198 | 0.091 |
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".