Collaborating to manage performance trade‐offs: How fire departments preserve life and save property
Bibliographic record
Abstract
Research Summary We examine how formal collaboration allows organizations to resolve performance trade‐offs that cannot be resolved informally. The theory is tested on U.S. fire departments, which pursue goals that sometimes conflict: reducing casualties and saving property. By relying on intrinsic motivation, informal collaboration reduces casualties and saves some property above what departments can achieve alone. Formal contracts are needed to achieve additional performance improvements on the goal of saving property. Contracts improve performance above what is accomplished informally by compelling collaboration even under casualty risk. Prior studies of collaboration that do not account for ex ante informality or performance trade‐offs may misstate the impact of collaboration on organization performance. Management Summary Like many organizations, U.S. fire departments pursue multiple goals that sometimes conflict. For fire departments, these goals are reducing casualties and saving property. Goal conflict arises when firefighter lives are put at risk to save property. To improve performance on both goals, fire departments often collaborate with neighboring departments in nearby jurisdictions. In this paper, we examine how performance on both goals improves when departments collaborate informally through handshake agreements. However, performance in saving property—the goal that is less intrinsically motivating for firefighters—improves even more when the collaborating departments implement a formal contract. At the same time, casualties increase slightly. This is because a contract creates an obligation for an assisting department to save property even under a risk to firefighters' lives. The analysis shows how formal contracts are implemented to resolve trade‐offs that cannot be resolved informally. We conclude that the performance improvements associated with collaboration may be quite different than the improvements that follow the implementation of formal contracts. This is because contracts may deal only with marginal trade‐offs between the goals of the collaborators.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".