Introduction: Current Socio-Legal Perspectives on Dispute Resolution
Bibliographic record
Abstract
In recent years, there were increasing interests in quantitative survey research on experiences of legal problems and access to justice in an unprecedented number of countries. Such survey research was initially conducted in the U.K. and the U.S. and later in Canada, New Zealand and Australia, countries with the Anglo-American legal tradition. However, a similar survey was recently carried out in the Netherlands, Japan and Hong Kong, countries of the Civil Law tradition, some of them with Asian social background. Now we have fantastic opportunities for comparative studies of civil disputes and dispute handling behavior among countries with different socio-legal backgrounds. Drawing upon these survey data, we discussed on how experiences of legal problems and occurrences of disputes differ among countries, how legal machineries are used or not used to resolve disputes, how levels of satisfaction with outcomes differ, and research designs and quantitative analytical methods for future surveys. DOWNLOAD THIS PAPER FROM SSRN: http://ssrn.com/abstract=1941689
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".