Bibliographic record
Abstract
Since its appearance in 2003, avian influenza (AI) has been highly 'productive,' linking into a distinct form diverse elements, among which include: international health organizations and local farmers; governments and AI viruses; wildlife experts and public health practitioners; food regulatory bodies and ecologists; ornithologists and the poultry industry; ordinary citizens and, of course, poultry. From the perspective of an anthropology of thinking, 'AI' is intriguing because it escapes 'nature' and 'culture' concepts that traditionally ordered relationships between man and the natural world. Is 'AI' natural or cultural, animal or human, a blend of both, or something else entirely? The emergence of 'AI' does not only warrant the development of new conceptual frameworks, but arguably gives rise to new realities as they take shape from these conceptual frameworks. In the project undertaken here, I follow the emergence of form as I trace 'AI' through three different field sites: 1) poultry farms in the Fraser Valley of British Columbia, Canada; 2) the Department of Ecosystems and Public Health at the University of Calgary; and 3) a field school on ecohealth in New Brunswick. In each field site, I examine how 'AI' has allowed for emergent, still developing and potentially transient, orders of reality to come into being.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".