Fact‐Finding Effectiveness: Evidence from New York State Fact‐Finding Effectiveness
Bibliographic record
Abstract
In a collective‐bargaining environment characterized by increasing fiscal and taxpayer pressures, this article examines the continued viability of fact‐finding as a dispute‐resolution mechanism in New York State's public sector. This is an important question because fact‐finding is the final dispute‐resolution procedure for most unionized employees in New York and many other states. Whether fact‐finding effectiveness was measured by the proximity of fact‐finder recommendations to the final settlement or by outright acceptance of the fact‐finder report, regression results show that New York fact‐finding has successfully met the challenges of the intensified environmental pressures in the 1990s. No significant decline was found in its ability to move parties toward the compromise outcome. Part of the fact‐finding's continued success can be attributed to the policy shift by New York's Public Employment Relations Board (PERB) in 1991 in the role of the fact‐finder from an accommodative to a more adjudicative function. The well‐reasoned adjudicative fact‐finding report has more potential to bring public pressure to bear on the extreme positions of the parties. Mediation was better left to the professional PERB mediators. Finally, it also was found that fact‐finders who were full‐time neutrals were more effective under this more adjudicative style.
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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 | 0.027 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".