Aims and Scope
JEPR publishes formal, methodological and empirical research on how incomplete, conflicting and underdetermined evidence is represented, measured and used in decisions by human and artificial systems. Contributions examine the conditions under which epistemic distinctions preserve information, predict observable behaviour or improve decisions, and the conditions under which those distinctions fail or add no practical value. The journal welcomes relevant approaches from logic, statistics, measurement theory and artificial intelligence, with explicit connections to existing literature and appropriate comparisons.
Our shared question is when distinctions between kinds of evidence preserve information, predict behaviour or improve decisions, and when they fail. Formal illustrations and empirical validation are assessed according to their respective claims.
Editorial requirements
- Assessable contribution. Each submission states an explicit claim about representation, measurement or decision and provides the evidence appropriate to that claim. Formal work includes a precise result and an explicit interpretation or example; empirical work includes an operational definition and a reproducible analysis.
- Informative positive and negative findings. Evidence for a distinction, evidence against it and evidence identifying its limits are evaluated by the same standards. Equivalence claims require a justified margin and suitable analysis; lack of statistical significance alone is insufficient.
- Verifiable support. Referees receive the data, code, proofs or computational artefacts needed to assess the main claims, subject to justified restrictions. Reports state which results were independently checked and which could not be checked.
Sections
- Semantics of typed indeterminacy. Formal semantics that distinguish kinds of non-determination (annotated, paraconsistent, many-valued and evidence-based logics), with precise, relevant results, complete proofs and an explicit interpretation or example.
- Abstention and risk-coverage. Selective prediction, reject options and abstention, evaluated through risk-coverage behaviour rather than accuracy alone.
- Disagreement as signal. Human and machine disagreement treated as information: label variation, inherent disagreement, and insufficient versus contradictory evidence.
- Belief revision and epistemic update. Operators for belief change and epistemic update, with precise formal results or explicit tests of observable behaviour and limitations.
- Audit of uncertainty frameworks. Reproducible audits of how uncertainty frameworks are applied, including reanalyses of published studies.
- Decision under indeterminacy. Decision methods that use indeterminate or contradictory evidence, with fair comparisons against appropriate alternatives, including classical or probabilistic baselines where relevant.
- Negative results. Calculi that separate nothing and distinctions that collapse under measurement, reported with the same rigour as positive results.
- Evidential reasoning in educational and social measurement. Applied studies in education and the social sciences that test whether and when distinguishing lack of evidence from conflicting evidence changes a measurement or a decision. Held to specific requirements and limited to one third of each issue; see the section policy.
Editorial areas
The journal connects its eight sections through four areas: logic and foundations; measurement and statistics; AI evaluation and selective prediction; and education and social measurement. These areas guide subject matching and assessment. Supporting artefacts receive a reproducibility assessment appropriate to the contribution.
Audience and language
The journal addresses logicians, statisticians, and researchers in machine learning, decision science and education who work with indeterminate or contradictory evidence. It publishes in English.
