Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

A Multilevel Modelling Approach to Investigating the Predictive Validity of Editorial Decisions: Do the Editors of a High Profile Journal Select Manuscripts that are Highly Cited After Publication?

Journal of the Royal Statistical Society Series A: Statistics in Society(2011)10.1111/j.1467-985x.2011.00689.xSource: DataRank Database

A Multilevel Modelling Approach to Investigating the Predictive Validity of Editorial Decisions: Do the Editors of a High Profile Journal Select Manuscripts that are Highly Cited After Publication? is a research paper published in Journal of the Royal Statistical Society Series A: Statistics in Society (2011). On theSindex it has a DataRank of 0.598. It has been cited 53 times.

N/A

0.598DataRank · unranked

0.598

Open Access53 citations · base score 4.0

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

SummaryScientific journals must deal with the following questions concerning the predictive validity of editorial decisions. Is the best scientific work selected from submitted manuscripts? Does selection of the best manuscripts also mean selecting papers that after publication show top citation performance within their fields? Taking the journal Angewandte Chemie International Edition as an example, this study proposes a new methodology for investigating whether manuscripts that are most worthy of publication are in fact selected validly. First, the influence on citation of the accepted and rejected but then published elsewhere manuscripts was appraised on the basis of percentile impact classes scaled in a subfield of chemistry and, second, the association between the decisions on selection and the influence on citation of the manuscripts was determined by using a multilevel logistic regression for ordinal categories. This approach has many advantages over methodologies that were used in previous research studies on the predictive validity of editorial selection decisions.

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.598

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

Read the full methodology →