Peer review, bibliometrics and altmetrics ‐ Do we need them all?
Bibliographic record
Abstract
ABSTRACT This panel will present views and critical reflections on peer review, bibliometrics and altmetrics against the background of the latest developments and critique of the scientific system (e.g., replication crisis, DORA, open science, data manipulation). Peer review is the oldest form of monitoring the scientific process and research outcomes and is sometimes considered as the gold standard for evaluating quality. However, it also has its drawbacks; therefore, new forms of peer review are being explored. Bibliometrics, comparative statistics based on publication and citation counts, was introduced as a more objective evaluation method, which is applicable on meso and macro levels of research producing units but is not without problems either. Citation counts as a proxy of quality; the impact factor and the h‐index are some of the most controversial subjects of research evaluation today. The newest addition to the evaluation toolbox are altmetrics, impact measures based mainly on social media activity. Altmetrics have pros and cons as well. None of the measuring devices is perfect, and rather than replacing they complement each other. By drawing on indicators from all three aspects of research evaluation, negative and adverse effects are limited.
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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 | Not applicable | low |
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 | 0.054 | 0.363 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.100 | 0.480 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".