Consequences: the impact of law and its complexity
Bibliographic record
Abstract
Canada and the United States increasingly rely on law to grapple with complex societal issues. What is the impact of this growing dependence on law and legal systems? W.A. Bogart offers a timely and erudite investigation of the impact of law on societies, and how this excessive reliance on law, particularly litigation, has generated difficulties in achieving consensus regarding issues of domestic policy. Focussing mainly on the United States as the center for post World War II legal culture, the book takes into consideration other western countries, and allows the reader a comparison of legal systems. Consequences begins by documenting the growth of law and the reasons for its enhanced influence; the book then discusses the complex meanings of impact and the substantial difficulties in gauging outcomes produced by law. Bogart illustrates his discussion with five case studies, documenting law's complex ties to economics, education, and political issues, and asserts that positive outcomes have occurred despite litigation's disappointing record. Consequences is a timely, important and interdisciplinary contribution to the study of law and sociology, and will make a substantial addition to the studies of law and society.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".