Shaping people-place bonds in citizen science: a framework for analysis
Bibliographic record
Abstract
Hands-on, out-of-doors, environmental citizen and community science invites a wide range of publics to participate in data collection in the spaces and places local to them; that is, placed-based science. Understanding whether and how participants are attached to those places can inform all aspects of project/program design. Building on sense of place theory, we advance a multidimensional framework from which to conceptualize, evaluate, and describe people-place bonds in environmental citizen science, using survey responses from participants in the Coastal Observation and Seabird Survey Team (COASST). Results provide evidence that place attachment is strong, with aspects of place identity resonating much more strongly than place dependence. We explored six dimensions of place attachment relevant to COASST participants and found attachment to be asymmetrically multidimensional, dominated by nature-environment bonding, with secondary strengths in science community bonding, self-identity, and science affinity. The participant population displayed relatively low attachment strength along the friends and family axis, and no resonance within the dimension of social rootedness. We also found shifts in the multidimensional “shape” of attachment as a function of time in the program, with individuals persisting over 10 years stronger in almost all dimensions. These findings raise important questions for the field of participatory science about the significance of people-place bonds, how place attachment shifts over time, and the impacts of that attachment on citizen science outcomes around behavior, decision making, and policies connected to place.
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 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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".