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Record W7127131346 · doi:10.18357/wg23201832

Analytical framework for community resilience

2018· article· W7127131346 on OpenAlexafffundabout
Nushrat Jahan, Leith Deacon

Bibliographic record

VenueWestern Geography · 2018
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsUniversity of AlbertaUniversity of Toronto
FundersMitacsKillam TrustsUniversity of Northern British Columbia
KeywordsDiversification (marketing strategy)Resilience (materials science)BustBoomCommunity resiliencePsychological resilienceNatural resource

Abstract

fetched live from OpenAlex

Resource-based communities (RBCs) are a common feature of Canada’s economic landscape. Community resilience is critical for RBCs, where economic cycles associated with the fluctuations in prices of natural resources in international markets occur regularly. This paper presents an analytical framework of community resilience of RBCs in the Canadian context. Using the Town of Devon, Alberta as a case study and publicly accessible sources of data, indicators of community resilience for an RBC are identified. The results highlight that the case study community has typical demographic and economic characteristics of a boom and bust cycle in an RBC for majority of the indicators of resilience. Additionally, results also show that existing regional and municipal policies focus on a diversified economic base, improved municipal facilities, and environmental management. This case study suggests the need for further research to examine a RBC’s long-term growth trajectory, sensitivity to distance from centres of business and trade, and the impact of policy directives for diversification and environmental protection.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.153
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.005
Science and technology studies0.0040.007
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.058
GPT teacher head0.299
Teacher spread0.241 · 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 designTheoretical or conceptual
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
Published2018
Admission routes3
Has abstractyes

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