Perils of conversation: #MeToo and opportunities for peacebuilding
Bibliographic record
Abstract
Purpose The purpose of this paper is to discuss the opportunities for future organizational and leadership research presented by positioning dialogue related to the #MeToo movement within a peacebuilding agenda. The #MeToo movement raised public consciousness about the pervasiveness of sexual assault and harassment in schools, workplaces and other institutions by an international social media campaign. However, subsequent discussions are often charged with hostility and outrage that result in divisiveness within workplaces and other settings that can further silence about these issues. The authors argue a community peacebuilding framework can create space to have difficult conversations to further the efforts of the #MeToo movement. Design/methodology/approach The authors will discuss the implications of a peacebuilding framework by the discussion of a case study in a rural setting to highlight the ways in which community conversations necessary to further the goals of the #MeToo movement. Findings A grassroots community peacebuilding framework can present opportunities for victims, offenders, family members and the community to voice expressions harms experienced and to enable processes of accountability. Promotion of a positive relational peace includes opportunities for education, skill development and conflict resolution that are healing and transformative for individuals and communities. Research limitations/implications The systemic social and cultural change required to prevent sexual harassment and sexual assault must happen within face-to-face relationships within community dialogue. Practical implications The authors argue a critical relational peace lens offers an emancipatory framework that can initiate change from the bottom up to facilitate social healing and further the efforts of the #MeToo movement. Social implications Grassroots peacebuilding invites a relational peace established by dialogue within communities. This dialogue is often not easy but is recognized as essential to establishing trust, resolving conflict and fostering community integrity. Originality/value In this paper, the authors offer a community peacebuilding framework to provide skill development and a vision necessary to host difficult conversations to inform the next wave of the #MeToo movement.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.036 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".