Practicing Conflict Resolution and Cultural Responsiveness within Interdisciplinary Contexts: A Study of Community Service Practitioners
Bibliographic record
Abstract
Workplace conflict is a significant issue for community service professionals. As more professions work toward developing interdisciplinary teams and culturally responsive practices, the potential for the escalation of conflict may increase as different professional value systems and conflict management strategies converge. However, although they are often expected to respond proficiently to conflicts, many community service professionals may not have had sufficient training in policies, practices, and structures that can provide alternative and transformative approaches to conflict management in diverse contexts. This article presents results of an exploratory study with interdisciplinary community service students who took part in a conflict resolution course at a diverse university in a metropolitan city in southern Ontario, Canada. The findings show that most of these community service–related professionals dealt with conflict on a daily basis, much of which was escalated by cultural conflict, lack of professional resources and development, and limited training in transformative peacebuilding practices. Most participants found that cultural diversity and gender influenced how they responded to conflicts in their various settings. The findings have important implications for how issues of culture and diversity are addressed and included in conflict resolution training programs.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".