How Bizarre, How Bizarre, How Bees Are: Domus and Umwelt in the Multispecies Entanglements of Humans and Honeybees
Bibliographic record
Abstract
Relationships between humans and honeybees are complex because they are deeply entangled. These entanglements take on many forms, such as those present in the honeybees’ self-centered worlds—umwelt—as well as the intersections in their mutually constituted lived world, or their domus. As honeybees are involved in pollinating up to 85 percent of the world’s agricultural crops, understanding these entanglements is a vital component of environmental anthropology. To highlight these interconnected relationships as they apply to commercial beekeepers, this paper explores the case study of an Australian almond farmer named Mark deCaux who has incorporated beekeeping into his agricultural practices to ensure his crops are adequately pollinated. Since adopting beekeeping, his crops have grown his apiary to a commercial-sized practice. Through the lens of his experiences, the concepts of umwelt and domus emphasize the intricate negotiations between humans and honeybees that constitute their mutually entangled relationships.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".