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The Human Ecology of Disaster Risk in Cold Mountainous Regions

2016· reference-entry· en· W2597461345 on OpenAlexaff
Kenneth Hewitt

Bibliographic record

VenueOxford Research Encyclopedia of Natural Hazard Science · 2016
Typereference-entry
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGlacierPermafrostHuman settlementGeographyPhysical geographySnowHazardClimate changeHabitatEcosystemEcologyEnvironmental resource managementEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Abstract A range of environmental and social dimensions of disasters occur in or are affected by the mountain cryosphere (MC). Core areas have glaciers and permafrost, intensive freeze-thaw, and seasonally abundant melt waters. A variety of cryospheric hazards is involved, their dangers magnified by steep, high, and rugged terrain. Some unique threats are snow or ice avalanches and glacial lake outburst floods. These highlight the classic alpine zones, but cryospheric hazards occur in more extensive parts of mountain ecosystems, affecting greater populations and more varied settings. Recently, habitat threats have become identified with global climate warming: receding glaciers, declining snowfall, and degrading permafrost. Particularly dangerous prospects arise with changing hazards in the populous mid-latitude and tropical high mountains. Six modern calamities briefly introduce the kinds of dangers and human contexts engaged. Disaster style and scope differs between events confined to the MC, others in which it is only a part or is a source of dangerous processes that descend into surrounding lowlands. The MC is also affected by non-cryospheric hazards, notably earthquake and volcanism. In human terms, the MC shares many disaster risk issues with other regions. Economy and land use, poverty or gender, for instance, are critical aspects of exposure and protections, or lack of them. This situates disaster risk within human ecological and adaptive relations to the predicaments of cold and steepland terrain. A great diversity of habitats and cultures is recognized. “Verticality” offers a unifying theme; characterizing the MC through ways in which life forms, ecosystems, and human settlement adjust to altitudinal zones, to upslope transitions, and the downslope cascades of moisture and geomorphic processes. These also give special importance to multi-hazard chains and long-runout processes including floods. Traditional mountain cultures exploit proximity and seasonality of different resources in the vertical, and avoidance of steepland dangers. This underscores sustainability and changing risk for the many surviving agro-pastoral and village economies and the special predicaments of indigenous cultures. Certain common stereotypes, such as remoteness or fragility of mountain habitats, require caution. They tend to overemphasize environmental determinism and underestimate social factors. Nor should they lead to neglect of wealthier, modernized areas, which also benefit most from geophysical research, dedicated agencies, and expert systems. However, modern developments now affect nearly all MC regions, bringing expanding dangers as well as benefits. Threats related to road networks are discussed, from mining and other large-scale resource extraction. Disaster losses and responses are also being rapidly transformed by urbanization. More broadly, highland–lowland relations can uniquely affect disaster risk, as do transboundary issues and initiatives in the mountains stemming from metropolitan centers. Anthropogenic climate warming generates dangers for mountain peoples but originates mainly from lowland activities. The extent of armed conflict affecting the MC is exceptional. Conflicts affect all aspects of human security. In the mountains as most other places, disaster risk reduction (DRR) policies have tended to favor emergency response. A human ecological approach emphasizes the need to pursue avoidance strategies, precautionary and capacity-building measures. Fundamental humanitarian concerns are essential in such an approach, and point to the importance of good governance and ethics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.005
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.316
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2016
Admission routes1
Has abstractyes

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