Interest‐based bargaining: achieving improved relationships through collaboration
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate the use of interest‐based bargaining as opposed to traditional adversarial bargaining when negotiating collective agreements in unionized libraries. Design/methodology/approach This paper explores existing theory, research and practice on the use of interest‐based bargaining in labor negotiations. It accomplishes this goal through a review of relevant literature and case studies, concentrating on practical application in a typical library labor environment. Two specific implementation models are described and the benefits and limitations of interest‐based bargaining are presented, supported by evidence from multiple examples. Findings Interest‐based bargaining offers significant benefits to organizations that adopt this approach when negotiating collective agreements, including improved working relationships between management and workers and longer term solutions to problems and issues. Library managers in unionized libraries could realize these benefits by implementing interest‐based bargaining strategies in coordination with a supportive union. Research limitations/implications There is very little literature dealing with interest‐based bargaining in a library environment, so extrapolations from other industry examples have been used to illustrate the strengths of this approach. Practical implications Given the potential benefits of using collaborative negotiation approaches, and the increasing adoption rate in other labor industries including comparable public sector organizations, libraries have much to gain by investigating this option as either an alternative or an adjunct to traditional adversarial collective bargaining. Originality/value The paper presents a solid case for exploring the use of interest‐based bargaining in a library context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".