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Record W2981654640

The Role of Place Attachment in Volunteer Monitoring: A Transnational Study of Engaging Volunteers

2019· article· en· W2981654640 on OpenAlexaboutno aff
Rachel Pierson

Bibliographic record

VenueScholarWorks -A service of University of Vermont Libraries (University of Vermont) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVolunteerPlace attachmentPsychologySocial psychologyPublic relationsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

While many studies have identified motivations for public participation in scientific research, few have sought to understand the role that place attachment may play as a potential driver of initial or sustained participation. This study will apply a mixed-method approach to assess and compare the motivations of new and continuing volunteers in stream-based water monitoring programs in three countries: the United States, Canada, and New Zealand via: 1) surveys of stream-based volunteer monitoring groups, and 2) follow-up interviews with a subset of survey participants. Survey data collected to assess volunteer motivations will incorporate place attachment items as a metric to determine volunteers’ level of attachment with a particular site or stream. Interviews will be used to expand upon survey results. This research aims to determine the extent to which place attachment influences participants’ decision to volunteer, and if the level of such attachment may change over time. Subsequently, advocacy for protection of local natural areas is a potential benefit of volunteer monitoring that may be influenced by volunteers’ attachment to a specific site or place. Volunteer monitoring programs could be strengthened by focusing on the aspects of volunteering that draw participants and keep them engaged as well as identifying if place attachment has an influence on creating support to protect local natural areas.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.445
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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 teacher head, 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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueScholarWorks -A service of University of Vermont Libraries (University of Vermont)Same topicPlace Attachment and Urban StudiesFrench-language works237,207