Organizational Structures and Data Use in Volunteer Monitoring Organizations (VMOs)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Complex environmental problems call for unique solutions to monitoring efforts alongside developing a more environmentally literate citizenry. Community-based monitoring (CBM) through the use of volunteer monitoring organizations helps to provide a part of the solution, particularly when CBM groups work with research scientists or government managers. This study of volunteer monitoring organizations (VMOs) active in 2009 in the United States was conducted via survey in order to better understand the organizational structure, data collection procedures and data use of water-quality monitoring by volunteers, focusing on North Carolina. Organizational structures and origins of monitoring groups are discussed and reveal a wide variety of types and history of programs. Data collection procedures including required training and quality assurance were explored and discussed through the survey. Many groups require training of a varied type, but fewer complete quality assurance plans. Multiple types of volunteer monitoring data uses were indicated, including management and research. This study suggests a lack of structure at the state level may hinder the usefulness of data collected for purposes other than local information and environmental education. Cooperation between research scientists and VMOs may aid organizations in publishing more of their data and developing a quality assurance plan.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it