Bibliographic record
Abstract
Social context of natural disaster Danielle Maltais, Ph.D., Simon Gauthier, M.Sc. University of Québec in Chicoutimi (UQAC), Social Sciences Department, Social Work Teaching Unit, 555 Boulevard de l'Université, Chicoutimi, Québec, Canada, G7H 2B1, danielle_maltais@uqac.ca During the last few years, several countries in North America as well as in Europe or Asia were exposed to catastrophes that can be described as macrosocial catastrophes since a large number of people were affected. The death of an important number of people during the Katrina hurricane, the 2003 summer heat wave in Europe and the 2004 tsunami in Indonesia unfortunately showed that several countries and communities, even the most developed, are very badly prepared, in the event of a natural disaster, to protect and help its citizens and more specifically vulnerable people such as the old or the poor as well as lonely or sick people, those with reduced mobility or living in unsuitable housing conditions. Natural disasters are never completely quite so because of the frequency of the disasters as well as their human, community and social consequences and the extent of the subsequent material damage. They can be regarded as the result of human factors related to the deployment of ill advised activities for the environment (hasty urbanization and industrialization, deforestation, construction in zones at risk, etc), socio-economic conflicts (wars, political conflicts, displacement of segments of the population in environments at risk) or unequal distribution of economic, social and cultural resources between individuals, communities and countries. In social work, when we examine the causes and consequences of disasters on people's health and social activities, it is important to consider the notion of individual and social vulnerability of the people as well as the concepts of human adaptation to stress and impact strength. This communication will mainly make it possible to place the consequences of natural disasters in their social context and discuss the repercussions of this concept of reality on workers training and guiding.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.128 | 0.018 |
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".