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Record W2610571698

After the Tornado: An Exploration of Capacity and Vulnerability on Community Engagement in Goderich

2013· dissertation· en· W2610571698 on OpenAlexaboutno aff
Katherine E. Laycock

Bibliographic record

VenueThe Atrium (University of Guelph) · 2013
Typedissertation
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTornadoVulnerability (computing)Environmental planningGeographyPolitical scienceMeteorologyComputer securityComputer science
DOInot available

Abstract

fetched live from OpenAlex

This thesis is an investigation of the multi-dimensional impact of disaster on community engagement with respect to capacity and vulnerability factors. The historic community of Goderich, Ontario, ravaged by an F3 tornado August 21, 2011, was the study population. A mixed-methods approach utilizing surveys, semi-structured interviews, and key informant interviews was employed in an effort to yield a more\nconfident set of data and help facilitate understanding. Testing results revealed that the community was very aware of its capacities and vulnerabilities and utilized the disaster situation to affect positive change in these conditions. However, disaster itself was only found to stimulate engagement patterns in its immediate aftermath. Therefore, while disaster does not adversely affect community engagement, it also does not encourage sustained engagement activity. It does, however, stimulate extended associations of connection to the community, which may hold the key to long-term engagement motivation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.006
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.107
GPT teacher head0.331
Teacher spread0.224 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2013
Admission routes1
Has abstractyes

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