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Record W2953741938 · doi:10.1111/cag.12542

Does time heal all wounds? Restoring place attachment in Halifax's Point Pleasant Park after Hurricane Juan

2019· article· en· W2953741938 on OpenAlexfundvenueaboutno aff
Patrick Charles Lewis Larter, Jason Grek‐Martin, Amber Silver

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNova Scotia Health Research Foundation
KeywordsPlace attachmentFeelingNational parkPsychologyDistressHistorySocial psychologyGeographyArchaeologyPsychotherapist

Abstract

fetched live from OpenAlex

On September 29, 2003, Hurricane Juan profoundly altered Halifax's Point Pleasant Park, resulting in feelings of solastalgia (the distress caused by perceived negative changes to a beloved place) as park users mourned the loss of a place that held great significance for the city. Starting in 2008, over 100,000 trees have been planted in order to restore the original Acadian mixed forest to this landscape. Drawing on scholarly literature focused on place attachment and disaster recovery, this paper utilizes interviews (n = 11) and online surveys (n = 79) to determine whether long‐term park users have re‐established positive place attachments in conjunction with the park's restoration. Our results indicate that participants have largely overcome their solastalgic outlook and restored meaningful place attachments to the park. Unexpectedly, our results also suggest that long‐term participants have current place attachments that appear stronger than the place attachments expressed by participating short‐term users, who never experienced the traumatic impact of Hurricane Juan on Point Pleasant Park.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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 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

Citations9
Published2019
Admission routes3
Has abstractyes

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