The Process of Conflict Resolution in Public Project Disputes: Analysis by Settlement Methods

This study examines thirteen recent public dispute cases in Korea with the objective of analyzing the process of conflict resolution and thereby assessing the role of alternative dispute resolution (ADR) in public disputes. The focus is on dispute cases related to development of, or site selection for, public projects. Based on detailed accounts of events, the nature of the conflict, parties involved, pattern of actions taken, and final outcomes are analyzed. Only five cases were settled by ADR methods, indicating that ADR is rather ineffective in public conflicts. ADR seems to work better in locally confined, structured conflicts where participatory processes are used. An identifiable pattern in attempts at ADR is that ad-hoc committees are formed but often fail to reach agreement or are seen as lacking legitimacy, authority, and impartiality. Policy implications drawn from this study are that a higher priority should be given to developing community-based conflict resolution programs, that ADR should be incorporated into local government regulations to acquire greater legitimacy, and that conflict prevention procedures are a prerequisite for the success of conflict resolution programs

PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization

In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Inspired by this, we propose PromptStyler which simulates various distribution shifts in the joint space by synthesizing diverse styles via prompts without using any images to deal with source-free domain generalization. Our method learns to generate a variety of style features (from "a S* style of a") via learnable style word vectors for pseudo-words S*. To ensure that learned styles do not distort content information, we force style-content features (from "a S* style of a [class]") to be located nearby their corresponding content features (from "[class]") in the joint vision-language space. After learning style word vectors, we train a linear classifier using synthesized style-content features. PromptStyler achieves the state of the art on PACS, VLCS, OfficeHome and DomainNet, although it does not require any images and takes just ~30 minutes for training using a single GPU.Comment: Accepted to ICCV 2023, Project Page: https://promptstyler.github.io