Bibliographic record
Abstract
Collective storytelling has emerged as a vital praxis for BIPOC communities seeking to reclaim agency and counter pervasive silencing. This paper synthesizes over 2 decades of interdisciplinary scholarship to examine how shared narrative spaces function as pathways out of internalized voicelessness. Drawing on critical race theory, liberation psychology, and decolonial frameworks, I conducted a systematic literature review of empirical and theoretical works (2000–2025) that foreground collective storytelling modalities, such as story circles, digital oral histories, and community performance. The thematic analysis identified three core dimensions through which collective narratives engender empowerment: (a) emotional resonance and validation, wherein participants experience affective alignment and mutual recognition; (b) identity reclamation and re authoring, through which individuals reconstruct self narratives counter to dominant, oppressive discourses; and (c) solidarity networks and collective agency, wherein shared stories cultivate communal bonds that catalyze social action. Case studies from Black Truth Be Told initiatives to Indigenous oral history revitalization illustrate the transformative potential and contextual variations of these mechanisms. The study further highlighted emergent challenges, including sustaining intergenerational transmission and scaling digital platforms without diluting cultural specificity. Implications for practitioners and researchers are discussed, underscoring the need for intersectional, longitudinal evaluations of narrative interventions. By charting the contours of voices that echo, this review offers a conceptual scaffold for designing, implementing, and assessing collective storytelling as a decolonial strategy for healing, solidarity, and social change.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".