Bibliographic record
Abstract
The impact of disaster on human beings is tremendous. With the advent of disaster, individuals or family members will suffer life-threatening, property loss, organization breakdown, community relationship disintegration and Psychological trauma. Disaster relief provides an opportunity to social work. Through personal experiences on social work services provided in Sichuan disaster areas, this paper discusses social work theory, methods and goals on disaster relief. Key words: Disaster Relief; Community Reconstruction; Social Work Resume: L'impact de la catastrophe sur l'etre humain est enorme. Avec l'avenement de la catastrophe, des individus ou des membres de la famille subissent le danger pour la vie, la perte de biens, la decomposition des organisations, la desintegration des relations communautaires et les traumatismes psychologiques. Le secours en cas de catastrophe offre une occasion de travail social. Grâce a des experiences personnelles sur les services sociaux fournis dans les zones sinistrees du Sichuan, cet article traite de la theorie du travail social, les methodes et les objectifs de secours en cas de catastrophe. Mots-cles: secours en cas; de catastrophe; reconstruction communautaire; travail social 摘 要：災難對人類的影響巨大，在災難來臨之際人類個人或者家庭成員的生命受到威脅，財產遭受損失，組織 破碎社區關係解體，生態環境被破壞，造成心理創傷等等。災害救助為社會工作提供了契機。本文通過筆者在四 川災區社會工作服務的親身經歷，探討災難救助中社會工作理論、方法和目標。 關鍵詞：災難救助；災區重建；社會工作
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".