Resolving Mass Wrongs: A Command-Consensus Perspective
Bibliographic record
Abstract
This article explores how contemporary Western society conceptualizes and tries to resolve civil disputes arising from mass wrongs (a term that encompasses, but is broader than, mass torts). After reviewing complexities arising from such wrongs, including asymmetries in the size of parties, differential access to resources and power, and the tendency of at least some mass wrongs to cross political and geographic boundaries, the article sets out a spectrum of resolution options - called the command-consensus model - that are available to the parties.At the left end of the spectrum (the consensus end) are options with the highest degree of participant control and public scrutiny over process and outcome. These include such things as negotiation and boycotting of consumer products. The middle of the spectrum includes options that offer less party control and that usually involve a neutral third party, such as a mediator. Farther to the right are options such as arbitration and adjudication. At the extreme right (the command end) are public inquiries and democratic rule-making through legislation and regulation. These options are highly public and give the parties little individual control over the process or outcome.The spectrum, in fact, is anything but static: there is a significant interplay between its different parts. Using the example of mass wrongs, the article shows how resolutions achieved on one part of the spectrum can influence other parts. The emphasis is on the dynamic nature of the command-consensus model, the importance of being aware of a variety of options and of creative mixing of processes, and the advantages and disadvantages that various approaches can bring to the dispute resolution process.
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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.013 | 0.009 |
| 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".