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Record W4400490847 · doi:10.1080/10871209.2024.2376158

The pesky problem of defining a ‘pest’: testing the pest management attitudes scale in the United Kingdom

2024· article· en· W4400490847 on OpenAlexaff
Alexandra Palmer, Taciano L. Milfont, Joanne P. Aley, James C. Russell

Bibliographic record

VenueHuman Dimensions of Wildlife · 2024
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsMinistry of the Environment, Conservation and Parks
FundersRoyal Society Te Apārangi
KeywordsPEST analysisIntegrated pest managementScale (ratio)Environmental resource managementGeographyEnvironmental planningEcologyBusinessEnvironmental scienceBiologyMarketingCartography

Abstract

fetched live from OpenAlex

The Pest Management Attitudes (PMA) scale was developed to provide a unidimensional and versatile tool to assess attitudes toward introduced pests and their management. While the PMA has been tested and shown strong psychometric properties in samples from Aotearoa, New Zealand (NZ), it is only beginning to be used internationally. This study tested the utility and influence of wording of the PMA scale in the United Kingdom (UK), using a 2021 survey (N = 999) distributed via online platform Prolific. Two of the 9 PMA scale items were not appropriate in our UK sample. We posit that despite references to introduced and native species in the PMA wording, many participants completed the survey with human rather than biodiversity pests in mind. While the PMA remains a valuable tool for understanding attitudes toward pests and their management, wording may need modification to ensure that concepts translate cross-culturally to retain meaningful comparisons.

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)0.022
metaresearch head score (Gemma)0.099
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.099
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.092
GPT teacher head0.349
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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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