Approaching bibliometrics and prosopography: The comprehensive publishing landscape of CNPq (Brazil) and CONICET (Argentina) and its coverage in global databases
Bibliographic record
Abstract
Global databases such as Web of Science and Scopus have determined the standard indicators to measure the research output in national comparisons and its quality evaluation for decades. Already classic studies of science proved that this landscape of scientific production was distorted by using overly selective bibliographical indexes that were considered “international databases” while their coverage was severely limited. A relevant part of the bibliometric literature in the last 10 years has revolved around the limitations of these global data sources and the search for alternatives to explore more comprehensive universes of the scholarly output, considering all disciplines and languages. Particularly relevant in this debate are some recently created bibliographic services and search engines that provide new opportunities: Dimensions, Lens, Open Alex, CrossRef and Google Scholar. Our specific contribution to these studies relies on a methodological shift based on a convergence between prosopography and bibliometrics. For that end, we selected two countries that can be considered “peripheral centers” in the Latin American region. Firstly, we determined a universe of national researchers of the National Council for Scientific and Technological Development (CNPq, Brazil) and the National Council for Scientific and Technical Research (CONICET, Argentina). The target populations are composed of 10,619 tenured researchers at CONICET and 14,418 holders of the CNPq’s “research productivity fellowship”. Secondly, we built a database with their comprehensive publishing performance uploaded in the national curricular information systems, the Brazilian Lattes and Argentina’s SIGEVA, that includes metadata for all articles. After computational and manual data cleaning of this database, we retained a total 464,361 articles for Brazil and 81,005 for Argentina published in 2013-2020. The comparative study shows that Argentina and Brazil have similar patterns of coverage in the global databases, although they differ in terms of collaboration practices and national publishing.
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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.047 | 0.128 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".