The Atheists from Moscow: An Encounter with Colombian Former Combatants
Bibliographic record
Abstract
Julian is 32 years old. 1 He is a man with a mission.And a long beard, dishevelled hair, baritone voice and mischievous smile.He was once a librarian in a small town in southern Colombia.But no more.A few months before I met him, Julian left his stationary library job for a much more dynamic pursuit.His plan?To bring libraries to people instead of the other way around.Julian's "mobile library" vision was to get new books -and new ideas -to people living in the most remote areas of Colombia.Books on his back, Julian criss-crosses the veredas (rural villages) and the corregimientos (indigenous areas) by motorbike or on foot.He visits small villages of just one or two thousand people, scattered across the mountains.Villages, where there are no libraries, and where there may be no books.Villages, where schools have only textbooks, nothing more, and where it takes long walks for children to go to school.Julian tours his mobile library through the mountains of Cauca, a region in southwest Colombia.The Nasa indigenous people live in this region, as do guerrillas, paramilitaries and drug traffickers.Having travelled this route for a few months, Julian has befriended many traditional and community leaders and village teachers.Instead of arriving unannounced, he'll call these contacts in advance to let them know when he and his bounty of books will arrive.Once in a village, Julian invites the entire community to gather, introducing himself and his travelling library."Anyone who wants to borrow a book is welcome to!" Julian tells them.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".