Reflection on ResearchGate’s Terminating ResearchGate Score, and Interest Score, as Social Media Altmetrics and Academic Evaluation Tools
Bibliographic record
Abstract
ResearchGate (RG) is a popular academic social media networking platform for scientists, researchers, or academics (SRAs). RG automatically provides a metric, the RG Score, to each RG account holder that serves as a measure of that SRA’s “academic” worth, productivity, and interaction with other SRAs. In 2017, this metric was described by RG as “the RG Score takes all your research and turns it into a source of reputation,” indicating that “it is calculated based on the research in your profile and how other researchers interact with your content.” However, the precise manner in which the RG Score is calculated was never made public because it is a proprietary algorithm, and requests to RG to disclose details of the equations used to calculate it were not met. Not unsurprisingly, RG announced that it would be phasing out the RG Score in June 2022. This article examines what is known in the literature about the RG Score, which may be perceived as a skewed metric because it may add excessive weighting to select aspects, such as questions and answers, rather than to the published literature of an SRA. The RG Interest Score is also critiqued. An author-based metric such as the RG Score that reflects a realistic balance between the most important academic factors while downplaying fairly redundant aspects such as the volume of answers might benefit SRAs. As for any metric, the RG Score should not be used in isolation, be gamed, or used as the basis of any financial remuneration schemes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.057 | 0.443 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.012 | 0.035 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".