Diversity, Equity, Inclusion, and Belonging for Peace Leadership
Bibliographic record
Abstract
This paper examines the integration of diversity, equity, inclusion, and belonging (DEIB) principles with peace leadership to address societal divisions and foster sustainable harmony. By defining DEIB concepts and their interconnectedness with social justice, the authors highlight their critical role in shaping inclusive leadership practices. Diversity is presented as the acknowledgment of social differences; equity as the provision of fair opportunities tailored to individual needs; inclusion as the transcending of barriers to build coalitions; and belonging as a reciprocal sense of community and purpose. Rooted in Johan Galtung's distinction between negative and positive peace, peace leadership is positioned as essential for addressing structural violence and envisioning equitable societies. Through historical examples such as Nelson Mandela's leadership in post‐apartheid South Africa and the Northern Ireland peace process, the study underscores how DEIB‐driven frameworks advance mutual respect, reduce systemic inequities, and promote reconciliation. The paper argues for a peace leadership model that addresses root causes of conflict by intertwining social justice and moral imperatives, aligning with ethical traditions and sustainable development goals. The authors propose peace leadership as a transformative force capable of uniting diverse communities under shared principles of justice and inclusivity. By adopting DEIB principles, peace leaders can navigate contemporary societal challenges and catalyze progress toward a more harmonious global society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".