Power of the weak? Framing strategies in fiscal redistribution negotiations
Bibliographic record
Abstract
In fiscal redistribution negotiations, fiscally weaker sub-units aim to secure more funding but are disempowered by their dependency and lack of bargaining chips. What kind of negotiation strategies do fiscally weak actors rely on to maximize their bargaining positions in redistributive negotiations? The article puts forward a novel strategy of discursive framing whereby relatively powerless actors can reach successful agreements. Two strategies of framing, communitarian and coercive, are observed inductively through a comparative case study analysis of two instances of sub-federal redistribution negotiations in Canada. The findings reveal that ‘more is not always better’: more publicity and aggression can backfire, while communitarian strategies grounded in normative argumentation can prove effective despite their non-confrontational nature. Even a mixed communitarian-coercive strategy can prove effective given that sub-units remain consistent with their initial objectives and apply pressure incrementally. The lessons learned from these Canadian cases have broader implications for studying the dynamics of redistributive negotiations globally.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.032 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".