Bibliographic record
Abstract
Abstract In the wake of a civil war, local resources can play a potential role in shaping the recovery process by providing both old and new exegeses for the disturbing effects of the past. Using the case of Gorongosa, this article aims to explore the ways in which the war has impacted upon traditional medicine by creating Gamba spirits that cause havoc but can also transform the psychosocial hurts of war survivors. Historically, traditional healing practice was under the sole responsibility of the Dzoca, an ancestral spirit that for generations was embodied in living people through lineage descent to exercise its healing powers. There is consensus among healers that the Gamba spirit and healers emerged after the war and are rapidly spreading throughout Gorongosa. I explore the emic theories to explain the Gamba’s puzzling origins and the role they are currently playing in Gorongosa. Key words Central Mozambique • Gamba spirits and healers • post-war reactions • recovery strategies • traditional medicine Publications on the multiple and prolonged effects of wars and natural disasters and the individual and community recovery strategies from non-western societies are scarce. Very little is known about the consequences of trauma exposure in the psycho-socio-cultural dimensions of survivors’ Vol 40(4): 459–487[1363–4615(200312)40:4;459–487;038904] Copyright © 2003 McGill University transcultural psychiatry
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.615 | 0.367 |
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".