Making Continuous Improvement a Reality: Achieving High Performance in the Ottawa County, Michigan, Circuit and Probate Courts
Bibliographic record
Abstract
Today, a well-functioning court is expected to resolve large volumes of work in a fair and orderly way within demanding time frames. The overall goal is quality administration in all phases of court operations, yet achieving this goal in practice means navigating the shoals of tight budgets, workplace politics, and the heavy press of daily business. Courts are under enormous stress these days, and as a result it should come as no surprise that too many courts are infected with pessimistic court leadership. Winston Churchill is often reported to have said, “The pessimist sees difficulty in every opportunity, the optimist sees the opportunity in every difficulty.”1 A high-performance court makes the effort to reject pessimism as it looks to improve its administrative practices, even in tough times. To seize the opportunity for continuous improvement and rally support throughout the court, though, takes coordinated planning and follow-through. The bottom line is that court leaders need to work together at organizational change. In two recent articles in Court Review, we emphasized the necessity of judicial involvement and commitment if administrative improvement is to take hold and thrive. One point was that developing shared, court-wide agreement among judges on how court personnel should work together requires accepting two primary responsibilities: the role each judge has in making decisions and the administrative role judges have in making the system work. Judges benefit from orderly and stable court administration because it helps enhance preparation of all parties, augments the understanding of outstanding issues, and clarifies future procedural events necessary to bring final resolution. However, in any courthouse, making effective administrative practices a reality is a team effort; it requires conscious effort to organize work processes in a way that clarifies and engages the joint contributions of judges and court staff.2
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".