How are academic libraries in Spanish-speaking Latin America responding to new models of scholarly communication and predatory publishing?
Bibliographic record
Abstract
The topic of predatory publishing and ways to combat it is garnering considerable attention in many parts of the developed world, where academic librarians are emerging as leaders in this regard. However, less is known about how this phenomenon is playing out in developing regions, including Spanish-speaking Latin America. This study presents the results of a survey of 104 academic librarians in this region, along with follow-up interviews with seven respondents. The findings reveal that scholarly publishing literacy in general, and predatory publishing in particular, currently has low visibility in this part of the world, although there is growing recognition of and increasing concern about the issue. Although there is some debate about whether scholarly publishing literacy should be the sole responsibility of the library, many participants agree that the library has a role to play. Moreover, while most of the librarians who participated perceive that they have a solid knowledge of open access, they are less confident in their understanding of predatory practices and are seeking to increase their skills and knowledge in this regard to better support researchers at their institutions. To address this shortcoming, academic librarians in the region have expressed an interest in receiving training and in participating in international collaborations with other libraries that have already developed resources or programming in this area.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".