Libros Para Pueblos: An Exploratory Case Study
Bibliographic record
Abstract
This article is the result of an introductory two-year case study project that investigated community libraries supported by the not-for-profit organization Libros Para Pueblos (LPP) in the state of Oaxaca, Mexico. Libros Para Pueblos (LPP) is a largely volunteer-run library organization based in the capital city of Oaxaca de Juarez. In order to analyse the work of LPP we used Mostert & Vermeulen's (1998) nine areas for evaluation of community libraries. Over the past 20 years, the number of libraries the organization supports has grown from two to more than 70 throughout the state. The work that has facilitated this growth is carried out by a small Mexican staff, along with an Executive Committee and a Board of Directors made up of Americans and Canadians living in Mexico. The work is both time consuming and demanding, but it is fuelled by a positive reading ideology that is a result of memories of childhood reading. This motivation is shared by a network of 11 Mexican Regional Volunteer Coordinators who train and support local library workers. The local workers are often doing their tequio, which is a social requirement of working for one or two years in public service. We argue that the success of LPP libraries is influenced by: 1) an organizational structure that mandates Mexican leadership at the Executive level and in paid staff positions; 2) initiation from local representative; 3) the unique and complex socialist community configurations of the Oaxacan region; 4) a community of retirees who volunteer at many levels; and 5) national and international donations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".