Bibliographic record
Abstract
Human desire to interact with and learn about wildlife extends beyond charismatic megafauna. In the past two decades, insects have carved out a niche in the ecotourism sector. Entomotourism has emerged as a growing mainstream attraction for many tourists and insect enthusiasts alike (Lemelin 2015). This subsector of ecotourism encompasses a wide range of insect-related recreational activities, such as collection, educational or multimedia entertainment, and insect encounters in controlled environments, such as butterfly pavilions or insectariums (Lemelin 2013). Tourists all over the world may engage in activities such as photography, observation, entomophagy, and other forms of direct interaction with various types of insects. There are many explanations for this attention, including new technology (e.g., on-line chat groups and identification), the availability of digital images, and accessibility of new resources (e.g., field guides and insect organizations). Tourists are interested in insects for many reasons (Lemelin 2009). Some people love to see insects’ amazing color, behaviors, and unique features. Others like to see and photograph the spectacles of large congregations of insects. Some want to see rare species or to learn new things about insects. Still others want to learn about how insects can improve or maintain human well-being through pollination, food, and cultural heritage (Guiney and Oberhauser 2008, Durst et al. 2010, Yi et al. 2010). Many people like to learn about how indigenous peoples gained knowledge about or used insects (Hogue 1987, Huntly et al. 2005). Some people are even intrigued by the undesirable aspects of insects, such as crop consumption and biting humans (Kellert 1993, Huntly et al. 2005, Lockwood 2013).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".