Bibliographic record
Abstract
Every June for the last eight years, a coalition of commercial wool buyers, the Diné College Land Grant Office, and the Black Mesa Water Coalition has hosted a multi-site wool buy in the Navajo Nation of New Mexico/Arizona. Historically, the primary outlets for Navajos to sell their wool were trading posts and border towns, which paid far below market price. Over the last several years, the wool buy has effectively doubled the price per pound paid to Navajo producers by bringing them into direct contact with buyers. In June 2019, an estimated 160,000 pounds of wool were purchased from over 800 producers and shipped to Ohio for the next step in processing. Beginning with the 2019 wool buy, I have been conducting a commodity chain analysis, following the wool as it travels across the United States, through grading, scouring, spinning, and weaving. Grounded in site visits, interviews, and conversations with members of the supply chain, this project fleshes out the mechanics by which the historically-, regionally-, and culturally-specific Navajo wool is transformed into anonymous commodity. The material and its circulation become an anchor for understanding the embodiment of people, labor, and landscape in material. The process by which raw material becomes commodity is uneven and opaque, but this site-specific, fieldwork-based project begins to break open that black box and lay out the seemingly-endless threads which make up this complex tangle of history, material, culture, and politics, questioning the rhetoric of transparency that has become so prevalent in our conversations around the ethics of production.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".