Bibliographic record
Abstract
Examining the difficulties of conflict resolution, Transforming Conflict through Insight demonstrates how applying Bernard Lonergan’s philosophy of insight to mediation can lead to more productive and constructive negotiations. Kenneth R. Melchin and Cheryl A. Picard provide both an overview of conflict research and an introduction to Lonergan’s ‘insight theory,’ offering an outstanding piece of ethical philosophy and a useful method of mediation. Introducing readers to a method of self-discovery, the different kinds of operations involved in learning, and the role of feelings and values in shaping interactions with others in conflict, this volume also includes the practical experience of mediators who detail strategies of insight mediation for working creatively through conflict. Attending to the important role played by transformative learning in navigating conflicts, the authors show how insights and learning can move people past obstacles caused by feelings of threat. Informative, compassionate, and convincing, Transforming Conflict through Insight is a welcome resource for working to resolve difficulties in an ethical and educational manner.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".