Bibliographic record
Abstract
After a decade of chasing stories around the globe, intrepid travel writer Stephanie Elizondo Griest followed the magnetic pull home--only to discover that her native South Texas had been radically transformed in her absence. Ravaged by drug wars and barricaded by an eighteen-foot steel wall, her ancestral land had become the nation’s foremost crossing ground for undocumented workers, many of whom perished along the way. Before Elizondo Griest moved to the New York/Canada borderlands, the frequency of these tragedies seemed like a terrible coincidence. Once she began to meet Mohawks from the Akwesasne Nation, however, she recognized striking parallels to life on the southern border. Having lost their land through devious treaties, their mother tongues at English-only schools, and their traditional occupations through capitalist ventures, Tejanos and Mohawks alike struggle under the legacy of colonialism. Toxic industries surround their neighborhoods while the U.S. Border Patrol militarizes them. Combating these forces are legions of artists and activists devoted to preserving their indigenous cultures. Complex belief systems, meanwhile, conjure miracles. In All the Agents and Saints, Elizondo Griest weaves seven years of stories into a meditation on the existential impact of international borderlines by illuminating the spaces in between.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".