INTAROS Community-based Monitoring Experience Exchange Workshop Report, Fairbanks, Alaska, May 10, 2017.
Bibliographic record
Abstract
The workshop “Engaging Community-based Monitoring in Decision-Making and Assessment” was held May 10, 2017, in Fairbanks, Alaska. It offered an opportunity for practitioners of community-based monitoring (CBM) and observing programs to come together to exchange experiences and perspectives. Representatives from 10 CBM programs from Alaska and Canada were in attendance. Additional participants included researchers and government officials currently involved in CBM. The workshop was held at the University of Alaska Fairbanks International Arctic Research Center (IARC) as part of the Week of the Arctic activities that concluded the U.S. Arctic Council Chairmanship. Representatives from Arctic Council Working Groups, Alaska and US agencies, and the public were invited to a two-hour dialogue immediately following the workshop focusing on the use of CBM in decision-making and assessment. The workshop concluded that there are many excellent CBM programs in Alaska and beyond. They are actively documenting observations of a wide range of phenomena. While much progress has been made in this field, additional coordination and investment is needed. This can facilitate the ability of CBM programs to contribute relevant data and information in order to address the climate crisis that Alaska Native peoples are experiencing. Continued work and engagement is required to further develop responsive CBM programs in the Arctic. CBM programs are critical to support Alaska Native peoples in building a sustainable future that preserves culture and community. The proceedings describe the discussions at the workshop and dialogue and it outlines some of the good practices and needs that were identified.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.173 | 0.042 |
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".