The DOI matches its Crossref record.
Science as Social Knowledge: Values and Objectivity in Scientific Inquiry
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Likewise, authoritative knowledge emerges through organized critical scrutiny and communal validation rather than expertise alone [22].”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [22]
In the citing paper
“Knowledge acquires legitimacy by passing through institutional processes of review, debate, and revision.”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [22]
In the citing paper
“Longino's [22] social epistemology argues that epistemic authority emerges through organized criticism, peer evaluation, and communal scrutiny rather than through expertise alone.”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [22]
In the citing paper
“Across the generative pipeline, successive transformations from cultural corpora to datasets, from datasets to base models, and from base models to instruction-tuned systems enact epistemic and normative boundaries that delimit what forms of meaning become legible, what interpretive options are foregrounded, and which traditions of validation remain visible or are displaced [11,17,22].”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [22]
In the citing paper
“Expertise is therefore a necessary but insufficient condition for authority.”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [22]
In the citing paper
“This separation reflects Longino's [22] model of epistemic communities, in which reliability emerges through structured critical interaction.”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [22]
In the citing paper
“Longino's [22] model of epistemic communities and Serres's [35] notion of quasi-objects are relevant here: interpretive artefacts circulate within collectives, generating authority through structured participation rather than individual assertion.”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [22]
In the citing paper
“Rather than locating legitimacy in model behavior or output quality, the framework situates it in publicly inspectable practices of disclosure, review, and validation that organize how interpretive claims are produced, circulated, and authorized [11,22].”
From Who speaks when machines read scripture: boundary-making and interpretive authority in AI-mediated religious knowledge (2026) · cites this paper as [22]
In the citing paper
“Because scientific theories, hypotheses, and models are based on human perceptual categories, concepts, and assumptions, bias-free research is not possible [121, 125, 137].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [137]
In the citing paper
“Third, during the data-interpretation stage researchers make variousassumptions based on interpretive frameworks, analytical tools, concepts, and background theories (Elliott et al 2016, Longino 1990).”
From Data-Intensive Science and Research Integrity (2017) · cites this paper as Longino 1990
In the citing paper
“However, if an individual possesses too little background knowledge, he or she will not even be able to understand that the anomalous data are anomalous (cf.Lijnse, 1990;Longino, 1990).”
From The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. (1993) · cites this paper as Longino, 1990
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Metadata
- Authors
- Longino, H. E.
- Corresponding author
- Longino, Helen E.
- ORCID
- Longino, Helen E.
- Venue
- Princeton University Press eBooks
- Publisher
- Princeton University Press
- Published
- 1990-12-31
- OpenAlex
- W4300559119
- Cited by
- 3,092
- References
- 0
- Citations by year
- 2026: 244 · 2025: 296 · 2024: 188 · 2023: 169 · 2022: 212 · 2021: 160 · 2020: 145 · 2019: 173 · 2018: 128 · 2017: 86 · 2016: 73 · 2015: 100 · 2014: 60 · 2013: 80 · 2012: 58
- FWCI
- 81.08
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Princeton University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Logic and Philosophy of Science
- Social Sciences › Sociology and Political Science › Education, Politics, and Culture Studies
- Keywords
- philosophy of science, sociology of science, scientific knowledge, epistemology of science
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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