Investigating Science Researchers’ Presence on Academic Profile Websites: A Case Study of a Canadian Research University
Bibliographic record
Abstract
Researchers are increasingly using academic profile websites to organize and showcase their research outputs. Using the faculty at the science departments of the University of Saskatchewan, Canada as the study object, this research explores how science researchers used four academic profile websites: ResearchGate, Google Scholar Citations, Academia.edu, and ORCID. It was found that 78% of the researchers had established at least one academic profile, with ResearchGate being the most popular platform, Google Scholar Citations the second, followed at some distance by ORCID and Academia.edu. A high percentage of ORCID users did not list any of their publications, meaning their presence on ORCID was merely symbolic. We also found that the social interaction functions provided by ResearchGate were not well adopted. Findings from this study call for the improvement of the workflow of adding publications to ORCID profile.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".