Use Of Altmetric And Bibliometric Indicators To Measure Scientific Productivity In The Fields Of Life And Earth Sciences: Case Study From Haiti
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%.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".