La ciencia económica mexicana en el Sistema Nacional de Investigadores y su cobertura en Web of Science y Scopus, 1982-2020
Bibliographic record
Abstract
The percentage of members of the Mexican National System of Researchers (SNI) in the field of economics with publications indexed in canonical commercial bibliometric databases (BCCs, Web of Science, and Scopus) was estimated for the periods 1982–1998, 1999–2015, and 2016–2020. Previous studies determined that the evaluation mechanisms for entering the system have undergone changes during these three periods, with the valuation of publications indexed in the CBBs increasing, which should be reflected in an increase in the presence of SNI authors in these databases. However, it has also been demonstrated worldwide that the social sciences in general are poorly represented in BCCs. The present results show that, in fact, the economic research of SNI members is represented in BCCs in proportions similar to those of the social sciences in several European countries, Canada, and Australia: around 40% in WoS and 50% in Scopus. A high percentage of Mexican researchers with publications in these databases who do not belong to the SNI were also found, in line with previous studies on the evolution of the Mexican academic profession in general, which reported a much higher percentage of full-time academics with scientific publications compared to the percentage of academics belonging to the system. The method of searching for SNIs in the BCCs developed for this study is an original implementation of the Author Name Disambiguation (AND) problem.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".