We argue that inductive analysis (based on formal learning theory and the use of suitable machine learning reconstructions) and operational (citation metrics-based) assessment of the scientific process can be justifiably and fruitfully brought together, whereby the citation metrics used in the operational analysis can effectively track the inductive dynamics and measure the research efficiency. We specify the conditions for the use of such inductive streamlining, demonstrate it in the cases of high energy physics experimentation and phylogenetic research, and propose a test of the method’s applicability.
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At the same time, meeting the conditions for achieving it will take care of some of the difficulties OA typically encounters.
We define this sort of efficiency more precisely in the next section.
Citation patterns happen to supervene on the patterns of reasoning in the network in HEP case because of the external conditions we will specify. That is not always the case, because citation metrics can be messy and out of tune with the actual patterns of reasoning.
The results of this sort of research are typically published in science and research policy journals with some recent overlaps with social epistemology. Notable examples, relevant to our argument, include Maruyama et al. (2015), Carillo et al. (2013), Corley et al. (2006), and Martin and Irvine (1984a, b). All these methods of analysis, including computer simulations, were originally developed in Organization Theory in industrial economics (Peltonen 2016).
In other words, these hypothesis-driven simulations are based on theoretical considerations and can be used to show that a hypothesis about the efficiency of a scientific network is plausible. They stand in contrast to data-driven models, which are calibrated and tested with data.
The only recent significant exceptions are journals in astroparticle physics where HEP results are relevant and cited by physicists outside HEP laboratories.
It is also significant that the citations are tracked in the most advanced tracking system of that sort; INSPIRE-HEP categorizes citations into six categories, and has been in place for decades, preceding any currently used citation trackers such as Google or Thomson Reuter’s WoS.
See also Bornmann and Daniel (2008) for various reasons researchers cite papers for reasons other than acknowledgement of the quality of the results.
This is analogous to the statistical significance in Neyman–Pearson hypothesis testing. This fact could be exploited further, but it is not one of the goals of our analysis.
In disciplines in which several inductive methods are formally justified, the disagreement in the field will be justified as well. Thus, we will not be able to talk about reliable convergence of opinions.
See e.g. Dissertori et al. (2003).
Simplicity is defined as the number of constituents and the number of constituents per particle (Valdés-Pérez and Żytkow 1996, 54).
There is no need to spell out the proofs here; they can be found in Schulte (2000).
We can thus identify a temporal constraint on the applicability of the citation metrics and the reasons behind it: the long expiry dates of citation-metric analysis in certain cases (e.g. HEP) are determined by the justifiably long-term convergence on the results in the pursuit, as the revision of beliefs is justifiably minimized. Apart from establishing reliability of the results, IA has the potential to establish the computational properties of a scientific pursuit. For instance, Schulte has investigated the NP hardness of finding a simplest linear causal network from conditional correlations.
Experiments are similar—i.e. homogenous in terms of techniques and other traits of the experimental process—yet varied in terms of their efficiency.
Both are constructed in accord with even higher level of physical theory, Quantum Field Theory and Quantum Electrodynamics.
Most experiments do not purport to establish the existence of new particles; rather, they explore properties of the known ones. The Standard Model is a null hypothesis in the vast majority of experiments; it provides the expected background interactions, so the exploratory experiments that do not turn up new particles will be null experiments—but they will also provide important information on their properties (e.g. energy scales) that the model does not deliver. Even if an experiment that does not have any results of significance is reported, it will not result in the number or quality of citations that accompany experiments with confirmatory results.
This was certainly true of the citation patterns of the experiments from the late 1960s to the mid-1990s—the period analysed by the above-outlined studies; now research has become so centralized that essentially all particle physicists are engaged in one mega-project.
Historically, researchers constructed trees solely based on the 19S RNA, because of the difficulties obtaining sequence information (Yang et al. 2016).
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This work was presented at the conference “Formal Methods of Scientific Inquiry” held at the Ruhr-University, Bochum in 2017. We are greatful to the participants of the conference, audience at the Center for Formal Epistemology at the Carnegie Mellon University, Kevin T. Kelly, Oliver Schulte, Konstantine (Casey) Genin, anonymous referees and guest editors of the special issue for a number of comments and constructive criticisms. This work was supported by grant #179041 of the Ministry of Education, Science, and Technological Development of the Republic of Serbia.
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Perović, S., Sikimić, V. How Theories of Induction Can Streamline Measurements of Scientific Performance. J Gen Philos Sci 51, 267–291 (2020). https://doi.org/10.1007/s10838-019-09468-4
- Formal learning theory
- High energy physics