Citizen scientists and environmental stewards in Pacific Spirit Park
Bibliographic record
Abstract
Citizen science offers a unique opportunity to reconcile relational and substantive definitions of environmental expertise within STS scholarship. Additionally, because significant overlap exists between the work done by citizen scientists and environmental stewards, it may be prudent to incorporate environmental stewardship as a framework through which to understand citizen scientists, and, by extension, their capacity as environmental experts. As environmental expertise rests upon the ecological knowledge of a specific place, I conducted a six-month long participatory ethnographic study as a volunteer with the Pacific Spirit Park Society, a nonprofit organization of volunteer environmental stewards and citizen scientists in Pacific Spirit Regional Park in Vancouver, B.C. I also combined qualitative, semistructured interviews of my fellow volunteers with my own autoethnographic account of restoration and interpretation work to illustrate the process by which volunteers developed varying levels of expertise in regards to local ecological knowledge. Interlocutors also considered their responsibilities to both human and nonhuman members of their community, negotiated their own responsibilities as interpreters of ecological knowledge to the lay public, and reflected on how their lived experiences as boundary workers contrasted with traditional institutional epistemologies of science. I found that while there were volunteers who served as nexuses of information, the transfer of knowledge from one person to another was never unilateral, and a person’s expertise always developed in concert with that of another. The act of interpreting knowledge so as to make it accessible for fellow volunteers as well as for the public helped recruit people for the mission of care and stewardship. The lens of stewardship likewise enhances STS analyses of citizen science by providing context for how knowledge is applied and carried by practitioners.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".