Bibliographic record
Abstract
In this article we argue that mediation, as a process, is gendered. Although it is not a simplistic, binary opposition, mediation is primarily gendered and litigation is primarily gendered male. As a result of this gendering, mediation is devalued as compared to litigation in North America. In order to demonstrate this, in the first part of our article we focus on (i) the goals, (ii) skills, and (iii) language of mediation. When they are compared with litigation's goals, skills, and language, the gendering of both processes can be identified and the resulting devaluation of mediation observed. The goals, skills, and language of mediation are not simply traits that come naturally to some individuals based on gender. They are real and difficult skills to master, and therefore mediation education is implicated. In the second part of our article we focus on ways to improve the mediation education experience in law schools. We make some recommendations for mediation pedagogy and consider how law professors can better teach mediation so that law students understand and value the process. We conclude by arguing that mediation will continue to be devalued, especially as compared to litigation, until the process is revalued and mediatiors' contributions are appropriately evaluated. By demanding an assessment of mediation success that is not constricted by notions of female and male, both the profile and valuation of mediation and mediators will increase.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".