Systematizing Confidence in Open Research and Evidence (SCORE)

Abstract

Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creating and validating algorithms to provide confidence scores for research claims at scale. To investigate the viability of scalable tools, teams are creating: a database of claims from papers in the social and behavioral sciences; expert and machine generated estimates of credibility; and, evidence of reproducibility, robustness, and replicability to validate the estimates. Beyond the primary research objective, the data and artifacts generated from this program will be openly shared and provide an unprecedented opportunity to examine research credibility and evidence.

Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results

“…We employ the following heuristics to identify this data: We sample 100 PubMed sentences from Yu et al (2019) that are identified as having low causal content. We sample 50 titles from SBS paper present in Alipourfard et al (2021), as titles contain factors but rarely contain explicit associations and may be present in input data from automatically extracted text from PDFs. Finally we sample 50 first lines of SBS papers from Alipourfard et al (2021), as these lines frequently introduce topics or rhetorical questions which either lack associations or present highly hypothetical associations unlike those in our main corpus.…”

Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results

“…We employ the following heuristics to identify this data: We sample 50 PubMed sentences from Yu et al (2019) that are identified as having low causal content. We sample 100 titles from SBS paper present in Alipourfard et al (2021), as titles contain factors but rarely contain explicit associations and may be present in input data from automatically extracted text from PDFs. Finally we sample 50 first lines of SBS papers from Alipourfard et al (2021), as these lines frequently introduce topics or rhetorical questions which either lack associations or present highly hypothetical associations unlike those in our main corpus.…”

“…To extend relational scientific information extraction to specifically target scientific claims, we annotate SciClaim, 1 a dataset of 12,738 annotations on 901 sentences from expert identified claims in Social and Behavior Science (SBS) papers (Alipourfard et al, 2021), detected causal language in PubMed papers (Yu et al, 2019), and claims and causal language heuristically identified from CORD-19 abstracts (Wang et al, 2020).…”

“…To create SciClaim, two NLP researchers annotated 901 sentences from several sources: 336 from papers in Social and Behavior Science (SBS) with expert identified claims (Alipourfard et al, 2021), 411 filtered for causal language in PubMed papers (Yu et al, 2019), 135 containing claims and causal language identified from CORD-19 abstracts (Wang et al, 2020) with heuristic keywords, and 19 manual perturbations included only in training data.…”

From Unstructured Text to Causal Knowledge Graphs: A Transformer-Based Approach

“…The SciClaim scientific claim schema is designed to capture associations between factors (e.g., causation, comparison, prediction, proportionality), monotonicity constraints across factors, epistemic status, and high-level qualifiers. This model is used for qualitative reasoning to help characterize the replicability and reproducibility of scientific claims (Alipourfard et al, 2021;Gelman et al, 2021). We describe the entities, attributes, and relations of the schema, referencing the graphed examples rendered by our system in Figures 1, 2, and 3.…”