BOOM AND DOOM OF SCIENTIFIC RESEARCH IN VENEZUELA
Bibliographic record
Abstract
"Prior to the mid-20th century, scientific research in Venezuela was relatively scarce. However, political, economic, and social conditions carefully constructed in the second half of the century eventually led to an impressive boom in local science and technology. In those fifty years, academic, social, and humanistic research together with technological innovations were accomplished and put to effective use. Milestones in the process were: the creation of the Instituto Venezolano de Neurología e Investigaciones Cerebrales, subsequently restructurated as the Instituto Venezolano de Investigaciones Científicas where Marcel Roche led the generation of an ethos for the Venezuelan scientific researcher; or the professionalization of the activity through the creation of the Facultades de Ciencias, fundamental part of the outstanding expansion of the higher education system within the reformulated autonomous universities. Today, chimeric policies –socialismo del Siglo XXI– have ruined Venezuela, bringing about a crisis of unimaginable proportions, as demonstrated by the massive exodus of Venezuelans, including almost a quarter of its academic community."
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".