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Record W2041615754 · doi:10.1080/13683500802346169

Goodwill hunting: dragon hunters, dragonflies and leisure

2009· article· en· W2041615754 on OpenAlexaff
Harvey Lemelin

Bibliographic record

VenueCurrent Issues in Tourism · 2009
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsLakehead University
Fundersnot available
KeywordsRecreationOdonataTourismPopularityDragonflyGeographySociologyEcologyPolitical scienceBiologyArchaeology

Abstract

fetched live from OpenAlex

In Asia, insects have a long history of being a part of recreation and tourism activities, with some species such as rhinoceros beetles and dragonflies being raised as pets. While the role of insects in recreation and tourism (i.e. dragonfly gatherings, educational outings) is somewhat more modest in North America, Europe, and Australia, some of these activities are increasing in popularity. The availability of field guides, associations, and websites is helping to facilitate the growth of these leisure activities, and more specifically the viewing of Odonata (i.e. dragonflies, damselflies). Participant observations and interviews were used to provide an empirical understanding of how one particular insect order – Odonata attracts participants to recreation and tourism activities, fosters interests, and creates controversies (e.g. collecting). A theoretical framework provided by naturework, an interpretivsitic approach developed by Fine [(2003). Morel tales: The culture of mushrooming. University of Illinois Press.] is used to understand the philosophies involved in dragonflying. The conclusion highlights how new forms of recreation and tourism activities can promote greater awareness of insects.

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)0.001
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.009
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0020.001
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 head0.033
GPT teacher head0.357
Teacher spread0.323 · 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

Citations35
Published2009
Admission routes1
Has abstractyes

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