A Role for Judgment Aggregation in Coauthoring Scientific Papers (original) (raw)
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The nature of co-authorship: a note on recognition sharing and scientific argumentation
Synthese, 2014
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Three Criteria for Consensus Conferences
Consensus conferences are social techniques which involve bringing together a group of scientific experts, and sometimes also non-experts, in order to increase the public role in science and related policy, to amalgamate diverse and often contradictory evidence for a hypothesis of interest, and to achieve scientific consensus or at least the appearance of consensus among scientists. For consensus conferences that set out to amalgamate evidence, I propose three desiderata: Inclusivity (the consideration of all available evidence), Constraint (the achievement of some agreement of intersubjective assessments of the hypothesis of interest), and Evidential Complexity (the evaluation of available evidence based on a plurality of relevant evidential criteria). Two examples suggest that consensus conferences can readily satisfy Inclusivity and Evidential Complexity, but consensus conferences do not as easily satisfy Constraint. I end by discussing the relation between social inclusivity and the three desiderata.
Consensus versus Unanimity: Which Carries More Weight
British Journal for the Philosophy of Science, 2021
Around 97% of climate scientists endorse anthropogenic global warming (AGW), the theory that human activities are partly responsible for recent increases in global average temperatures. Clearly, this widespread endorsement of AGW is a reason for non-experts to believe in AGW. But what is the epistemic significance of the fact that some climate scientists do not endorse AGW? This paper contrasts expert unanimity, in which virtually no expert disagrees with some theory, with expert consensus, in which some non-negligible proportion either rejects or is uncertain about the theory. It is argued that, from a layperson's point of view, an expert consensus is often stronger evidence for a theory's truth than unanimity. Several lessons are drawn from this conclusion, e.g. concerning what laypeople should infer from expert pronouncements, how journalists should report on scientific theories, and how working scientists should communicate with the public.
Consensus formation in science modeled by aggregated bibliographic coupling
Journal of Informetrics, 2012
The level of consensus in science has traditionally been measured by a number of different methods. The variety is important as each method measures different aspects of science and consensus. Citation analytical studies have previously measured the level of consensus using the scientific journal as their unit of analysis. To produce a more fine grained citation analysis one needs to study consensus formation on an even more detailed level -i.e. the scientific document or article. To do so, we have developed a new technique that measures consensus by aggregated bibliographic couplings (ABC) between documents. The advantages of the ABC-technique are demonstrated in a study of two selected disciplines in which the levels of consensus are measured using the proposed technique.
There can be good reasons to doubt the authority of a group of scientists. But those reasons do not include lack of unanimity among them. Indeed, holding science to a unanimity or near-unanimity standard has a pernicious effect on scientific deliberation, and on the transparency that is so crucial to the authority of science in a democracy. What authorizes a conclusion is the quality of the deliberation that produced it, which is enhanced by the presence of a non-dismissible minority. Scientists can speak as one in more ways than one. We recommend a different sort of consensus that is partly substantive and partly procedural. It is a version of what Margaret Gilbert calls “joint acceptance” – we call it “deliberative acceptance.” It capitalizes on there being a persistent minority, and thereby encourages accurate reporting of the state of agreement and disagreement among deliberators.
Do we have good reason to believe a matter of scientific consensus?
Does the fact that the vast majority of expert scientists agree on a particular matter give us good reason to believe in it? I argue that it does if, and only if, three conditions are met: the group of scientists believing in the consensus is large, the consensus is the result of independent lines of reasoning, and an explicit effort has been made to disprove the consensus view.
When is Consensus Knowledge Based? Distinguishing Shared Knowledge from Mere Agreement
Synthese
Scientific consensus is widely deferred to in public debates as a social indicator of the existence of knowledge. However, it is far from clear that such deference to consensus is always justified. The existence of agreement in a community of researchers is a contingent fact, and researchers may reach a consensus for all kinds of reasons, such as fighting a common foe or sharing a common bias. Scientific consensus, by itself, does not necessarily indicate the existence of shared knowledge among the members of the consensus community. I address the question of under what conditions it is likely that a consensus is in fact knowledge based. I argue that a consensus is likely to be knowledge based when knowledge is the best explanation of the consensus, and I identify three conditions – social calibration, apparent consilience of evidence, and social diversity, for knowledge being the best explanation of a consensus.
Cross D. Truth, Belief and consensus
The rôles of truth, belief and opinion in the establishment of scientific consensus are examined, using contemporary case studies of confrontations between those with experience of major events and those who merely possess expertise founded on officially endorsed belief. Truth in science is becoming governed by the manufacture of artificial consensuses, defining political acceptability and pervading much of modern science. This has led to a fundamental conflict between science, politics and commercial vested interests. Now, machine intelligences are being developed, designed to identify “scientifically valid” consensuses and from these assess the “truthfulness” of statements published on Internet websites. These machine intelligences will then assign low “truth scores” to those sites judged to be unreliable. A short-lived human élite is emerging, to be rapidly replaced as selfgenerated algorithms of which we know nothing emerge—in other words, the new datocracy will be replaced by a fully machine-regulated arbiter of scientific truth. The concept is, however, founded on two critical inherent flaws—the underlying presumption that wisdom can be gained in the absence of an inherent sense of humour and a failure to identify logical questions that are impenetrable to scientific methodology. Most scientific committees are designed primarily to construct biased and self-protecting consensuses resistant to challenge. A prototypical example is the still current, but increasingly fragile and irrelevant, standard toxicological paradigm, which protects established consensuses favouring vested interests, but is indifferent to an emerging range of crucial biophysicochemical relationships, of which we are only just becoming aware.