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Record W4311681284 · doi:10.22215/etd/2022-15251

A Tale of Two Fisheries: Exploring Angler Behaviour that Informs Different Management and Conservation Goals

2022· dissertation· en· W4311681284 on OpenAlexaff
Jessika Guay

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsRecreationFishingSustainabilityFisheries managementFisheryRecreational fishingBusinessPsychological interventionEnvironmental resource managementPleasureCompetition (biology)Environmental planningTurnoverGeographyPsychologyEcologyEconomics

Abstract

fetched live from OpenAlex

Recreational fishing is an important activity primarily enjoyed for the purposes of pleasure or competition. Despite the many benefits of interacting with nature and harvesting wild food, recreational fishing presents a myriad of negative impacts to fish populations, thus requiring management interventions to ensure sustainability. Since management conservation measures typically involve angler compliance with regulations and voluntary adoption of proconservation behaviours, I analyzed social data from two fisheries facing contrasting conservation challenges to identify the prevalence of selfreported proconservation behaviours among recreational anglers. I further investigated the factors which influence such behaviours in an effort to dissect how certain desired behaviours may be encouraged to support management conservation goals and to contribute to knowledge surrounding angler behaviour. My results indicate high levels of participation in voluntary proconservation behaviours and may inform management strategies that would benefit from angler participation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.061
GPT teacher head0.319
Teacher spread0.257 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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