Highly specialized recreationists contribute the most to the citizen science project eBird
Bibliographic record
Abstract
Abstract Contributory citizen science projects (hereafter “contributory projects”) are a powerful tool for avian conservation science. Large-scale projects such as eBird have produced data that have advanced science and contributed to many conservation applications. These projects also provide a means to engage the public in scientific data collection. A common challenge across contributory projects like eBird is to maintain participation, as some volunteers contribute just a few times before disengaging. To maximize contributions and manage an effective program that has broad appeal, it is useful to better understand factors that influence contribution rates. For projects capitalizing on recreation activities (e.g., birding), differences in contribution levels might be explained by the recreation specialization framework, which describes how recreationists vary in skill, behavior, and motives. We paired data from a survey of birders across the United States and Canada with data on their eBird contributions (n = 28,926) to test whether those who contributed most are more specialized birders. We assigned participants to 4 contribution groups based on eBird checklist submissions and compared groups’ specialization levels and motivations. More active contribution groups had higher specialization, yet some specialized birders were not active participants. The most distinguishing feature among groups was the behavioral dimension of specialization, with active eBird participants owning specialized equipment and taking frequent trips away from home to bird. Active participants had the strongest achievement motivations for birding (e.g., keeping a life list), whereas all groups had strong appreciation motivations (e.g., enjoying the sights and sounds of birding). Using recreation specialization to characterize eBird participants can help explain why some do not regularly contribute data. Project managers may be able to promote participation, particularly by those who are specialized but not contributing, by appealing to a broader suite of motivations that includes both appreciation and achievement motivations, and thereby increase data for conservation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".