Wild Wild WEB: Wildlifew enthusiasts' use of the Internet
Bibliographic record
Abstract
e-learning aspects of wildlife enthusiasts, consisting of two (overlapping) segments: conservationists and general public wildlife enthusiasts. Both segments are passionate about wildlife and conservation issues, with conservationists being most passionate. Conservationists have used the Internet longer and also use email more. Work is still the main place to access Internet but 90% of the conservationists also have Internet at home. Most find information through specific searches, rather than visiting a familiar website, entering simple and general keywords. The Internet is seen as an awesome source of references whilst at the same time it is taken for granted. Although they are critical, people are not cynical about information on the Internet. They trust known and respected sites. Most people are hesitant about partaking in forums or chat groups and prefer email. Almost a quarter of the combined sample regularly downloads video and this much more Internet experienced group has a faster connection at home, look for wildlife information more often, are more likely to use chat groups, download photographs and they are more likely to use search engines. Potentially there is a large audience for a site like ARKive, consisting of motivated and experienced Internet users with a passion for conservation and wildlife issues.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.239 | 0.087 |
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".