Exploring Resilience and Communicated Narrative Sense-Making in South Africans’ Stories of Apartheid
Bibliographic record
Abstract
Guided by the communication theory of resilience (CTR) and communicated narrative sense-making (CNSM) theory, this study examines narrative resilience—or retrospective storytelling content that reflects a storyteller’s ability to reintegrate after difficult circumstances—in South Africans’ stories of apartheid. Participants were 17 South Africans who identified as Black, Colored, or Indian and endured the government-sponsored systematic oppression of apartheid. In semistructured interviews, participants told their stories of resilience in the face of the traumas of apartheid. Analyses illuminated four themes of communicated resilience: affirming identity anchor of toughness (i.e., showing strength in the face of adversity), foregrounding productive action (i.e., working to combat apartheid), putting alternative logics to work (i.e., focusing on positivity and hope), and crafting normalcy (i.e., normalizing life in apartheid). These themes support and advance CTR by exploring communicated resilience from a foundation of narrative theorizing and sociocultural understanding. Implications for furthering a social ecological conceptualization of resilience are investigated.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".