Interest‐based bargaining: efficient, amicable and wise?
Bibliographic record
Abstract
Purpose The purpose of this paper is to draw on previous research and propose a framework for evaluating interest‐based bargaining (IBB) around three criteria: efficient, amicable and wise, where mutual gains are not self‐evident. Design/methodology/approach This paper reviews both survey and case study research on IBB in the USA and Canada. Based on trends discerned in the data, the paper uses the three criteria to present research and propositions on evaluating the IBB process. Findings IBB connects front stage acts by negotiators during collective bargaining with backstage environments and fosters collaboration hinging on dialogue across competing values involving online and offline processes during negotiations. Where mutual gains are not self evident, there these findings underpin criteria for evaluating the IBB process’s potential to serve enduring values of industrial democracy and employee voice and the newer values of collaboration and partnership in strategic decision making. Research limitations/implications The amicable criterion predisposes the framework favorably towards amicable relations, which creates a favorable bias within the framework towards the IBB process when compared to other bargaining processes. There is a need for updated quantitative data on IBB trends at a national level, similar to the three FMCS surveys last reported in 2004, and a need for institutional linkages that will increase case study research on IBB, similar to recent research on Kaiser Permanente. Practical implications Negotiators, trainers and policy makers will gain from the criteria listed here to evaluate IBB where mutual gains are not self‐evident. Originality/value The framework presented in the paper advances an original framework to evaluate IBB.
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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 | 0.085 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".