La ciencia económica mexicana en el Sistema Nacional de Investigadores y su cobertura en Web of Science y Scopus, 1982-2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it