An Evaluation of the Cost of Family Law Disputes: Measuring the Cost Implication of Various Dispute Resolution Methods
Bibliographic record
Abstract
This study describes the results of a survey of family law lawyers and their views of the use of collaborative processes, mediation, arbitration and litigation in family law disputes. The study provides valuable insights into the costs of these processes, how long cases take to resolve, and lawyers' perceptions of their efficacy. It suggests that most lawyers are using, and prefer to use, dispute resolution processes other than litigation to resolve family law disputes. Four-fifths of respondents use mediation, almost two-thirds use collaboration, and almost one-third use arbitration. Moreover, almost all lawyers surveyed agree that people should attempt to resolve their dispute through another process before litigating, and almost three-quarters agree that, except in urgent circumstances, people should be required to attempt to resolve their dispute through another process before litigating. Three-quarters of lawyers also agreed that litigation should only be used as a last resort, when other dispute resolution processes have failed. In light of today's straitened budgetary resources, the findings from this study provide information that is useful for policymakers and program developers in identifying best practices in cost-effective dispute resolution methods.
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 | 0.054 | 0.292 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".