Use Of Altmetric And Bibliometric Indicators To Measure Scientific Productivity In The Fields Of Life And Earth Sciences: Case Study From Haiti
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 objective of this study was to carry out, based on certain bibliometric and altimetric indicators, a summary assessment of the scientific productivity of Quisqueya University’s researchers in 3 specific fields: agronomy, the environment and health. An experimental framework was designed and implemented based on the quantitative information available on the academic social network ResearchGate, and on SCOPUS and Google scholar, out of a total of 12,731 citations enumerated for Quisqueya University as of December 31, 2020, 19% were for the environment, 19.3% were for health, 59.9% for agronomy and 1.8% for other sectors. All the sectors recorded a significant increase for the RG score altmetric indicator and for the two bibliometric indicators: number of citations and H-index. The data collected were analyzed using XLSTAT and R software. The Kolmogorov-Smirnov normality test was applied for each of the indicators. Pearson's rank correlation was used to calculate the correlations between the altmetric indicator (RG-Score) from ResearchGate and the bibliometric indicators (citation and H-index) from Google Scholar and Scopus. A significant positive correlation of α = 0.918 was observed between the number of citations on ResearchGate and on Google Scholar. a result in the same direction (α = 0.991) is also observed between the number of citations on ResearchGate and on Scopus. These correlations allow us to conclude that the work of these researchers was cited in publications published in journals referenced in the Web of Science by a rate exceeding 90%.
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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 | 0.110 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.343 | 0.753 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.013 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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