Scientific recognition should be divisible because scientific progress is divisible. A discovery may depend on an original idea, an earlier theorem, a carefully maintained dataset, specialized software, experimental work, replication, […]
Should Teaching, Reviewing, and Dataset Maintenance Count as Scientific Output?
Yes—teaching, peer review, and dataset maintenance should count as scientific output when they produce identifiable, assessable, and reusable value. They should not necessarily receive the same kind or amount of […]
How Dependency Graphs Can Reveal Hidden Scientific Contributors
Scientific credit usually follows what is visible. The authors of a widely read paper receive citations, invitations, funding, and recognition. Yet many discoveries depend on people whose names never appear […]
Measuring the Impact of Research Software and Mathematical Libraries
Research software and mathematical libraries should be evaluated by the work they enable—not merely by papers that cite them, repository stars, or download counts. A useful impact assessment combines several […]
How Long Should Science Wait Before Rewarding a Discovery?
Science should not wait decades to reward a discovery—but it should not treat every new claim as permanently validated on day one. The best solution is staged recognition: provide an […]
What Is Scientific Merit? A Multi-Dimensional Definition
Scientific merit is the degree to which a research contribution reliably advances knowledge or improves the scientific process. It includes not only whether a result is novel, but also whether […]
Why Research Integrity Cannot Depend Only on Journal Editors
Research integrity cannot depend only on journal editors because editors see only one stage of a much larger research process. They generally evaluate manuscripts after experiments have been designed, data […]
How SciWard Could Reward Data, Code, Proofs, and Replications Separately
Scientific funding usually treats a research paper as the main unit of achievement. This approach overlooks much of the work that makes science possible: collecting reliable data, developing research software, […]
Why Funding Agencies Should Explain Every Rejection—and How SciWard Already Does This
Scientific funding agencies should provide a meaningful explanation for every rejected application. A rejection should identify the decisive reasons, the evidence or criteria behind them, and whether the problem concerns […]
Should AI Evaluate Researchers, Research Outputs, or Both?
AI should evaluate both research outputs and researchers—but not in the same way or with equal weight. Research outputs should be the primary unit of scientific evaluation. Papers, datasets, proofs, […]