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Record W68288154

Volunteering in fish-habitat rehabilitation projects in British Columbia

2007· dissertation· en· W68288154 on OpenAlexaboutno aff
Matthew Justice

Bibliographic record

VenueSummit (Simon Fraser University) · 2007
Typedissertation
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatRehabilitationFish <Actinopterygii>Fish habitatFisheryGeographyEcologyBiologyMedicinePhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

This research explores the motivations of volunteers within fish-habitat rehabilitation projects in western British Columbia. Literature on watershed partnerships, collaborative planning, co-management, volunteering, and interviews with key respondents were reviewed to formulate a series of questions about volunteer motivations. Interviews were conducted with respondents from a sample of urban and rural regions. Research results demonstrate motivations may be influenced by a diversity of reasons, but volunteers are driven chiefly by the desire to achieve positive resource outcomes. Individual empowerment is encouraged through positive reinforcement from others and by accomplishing group goals. Furthermore, government strategies can encourage volunteers with more long term and sustainable projects and by fostering greater trust with volunteers. This study considers the interaction of factors which influence the success of projects in effectively using volunteer contributions. The findings will be useful to agencies and project coordinators in attracting and retaining suitable volunteers.

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)0.005
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 score0.756
Threshold uncertainty score0.485

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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
Published2007
Admission routes1
Has abstractyes

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