Conceptualizing communities of place and practice: Applied Anthropology in a federal context
Bibliographic record
Abstract
As the first anthropologist hired in a NOAA Fisheries research laboratory devoted to fisheries science in support of fisheries management in federal waters, I faced many challenges. Federal laws required social impact assessment of fishery management plans, use of ecosystem-based management, and taking into account the impacts to fishers and fishing communities of both of these. But laws provide overarching guidance and policy documents need to refine the focus for research. I led the working group that wrote this guidance for fishing communities. Multidisciplinary teams of social and natural scientists today continue refining our understanding of the connections of communities to fisheries, whether commercial, recreational, or subsistence – or some combination. I also helped to develop a conceptual model for the Northeast Region ecosystem, and led an oral history research project examining the implementation across Europe of a NOAA Fisheries process model for ecosystem assessment. Although the hiring of anthropologists, sociologists, and others has become normalized, we are still few in number nationwide, so more work remains to be done.
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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.112 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".