Academic dependency: the influence of the prevailing international biomedical research agenda on Argentina’s CONICET
Bibliographic record
Abstract
Abstract Background Previous research within the field of health and biomedical sciences (HBMS) reported that its prevailing research agenda is determined by leading academic institutions and big pharma companies, prioritizing the exploration of novel pharmacological interventions over research on the socio-environmental determinants of disease. Unlike previous studies, which have relied primarily on qualitative analyses, the aim of this investigation is to quantitatively explore if that prevailing international research agenda influences research in semi-peripheral countries and to which extent. Methods We used the Web of Science database and the CorText platform to proxy the HBMS research agenda of a prestigious research institution from Latin America: Argentina’s National Research Council (CONICET). We conducted a bibliometric and lexical analysis of 16,309 HBMS scientific articles whereby CONICET was among the authors’ affiliations. The content of CONICET’s agenda was depicted through co-occurrence network maps of the most prevalent multi-terms found in titles, keywords, and abstracts. We compared our findings with previous reports on the international HBMS research agenda. Results In line with the results previously reported for the prevailing international agenda, we found that multi-terms linked to molecular biology and cancer research hegemonize CONICET’s HBMS research agenda, whereas multi-terms connecting HBMS research with socio-environmental cues are marginal. However, we also found differences with the international agenda: CONICET’s HBMS agenda shows a marginal presence of multi-terms linked to translational medicine, while multi-terms associated with categories such as pathogens, plant research, agrobiotechnology, and food industry are more represented than in the prevailing agenda. Conclusions In line with the academic dependency theory, CONICET’s HBMS research agenda shares topics, priorities, and methodologies with the prevailing HBMS international research agenda. However, CONICET’s HBMS research agenda is internally heterogeneous, appearing to be mostly driven by a combination of elements that not only reflect academic dependency but also economic dependency.
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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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".