Excellence or quality? Impact of the current competition regime on science and scientific publishing in Latin America and its implications for development
Bibliographic record
Abstract
The current competition regime that characterizes international science is often presented as a quest for excellence. It diversely affects research in Latin America and research in the Organization for Economic Co-operation and Development (OECD) countries. This article asks how this competition regime may orient the direction of research in Latin America, and to whose advantage. It is argued that, by relating excellence to quality differently, a research policy that seeks to improve the level of science in Latin America while preserving the possibility of solving problems relevant to the region can be designed. Competition, it is also argued, certainly has its place in science, but not as a general management tool, especially if the goal is to improve overall quality of science in Latin America. Scientific competition is largely managed through journals and their reputation. Therefore, designing a science policy for Latin America (and for any ‘peripheral’ region of the world) requires paying special attention to the mechanisms underpinning the production, circulation and consumption of scientific journals. So-called ‘international’ or ‘core’ journals are of particular interest as local, national, or even regional journals must struggle to find their place in this peculiar publishing eco-system.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".