Peircean Logic as Semiotic and Biosemiotics as Transdisciplinary Framework
Bibliographic record
Abstract
Peircean pragmaticism is close to Poppers critical rationalism in its fallibilism and evolutionary thinking. Peirce's synechistic continuity thinking includes a biosemiotics that has been develop over the last 30 years [2] represents a form of postmodern semiotic realism attempting to encompass qualitative and quantitative methods. Herby it represents a unity of science that the logical positivist could not produce and oters an alternative to constructivist postmodernism's many incommensurable small stories. So what is the ontology that makes such a common framework for quantitative and qualitative sciences possible? Peirce produces a transdisciplinary process philosophy through his triadic pragmaticist semiotic realism [1]. For Barbieri | and many other well-established researchers in the natural sciences | to be scientific is to be able to give mechanistic model explanations and eventually extend them with dualist theories of codes and information. In [3] I have argued that this foundation is not enough. It does not even embrace a systems and cybernetic foundation making self-organization possible. Peirce is inspired by German idealism, Especially Shelling and exchanges Hegel spirit and dialectics with his triadic semiotic logic. It is based on his three phaneroscopic (phenomenological) categories and views logic as semiotic and as a normative science for right thinking. He integrates this with empirical quantitative science, since he was educated as a chemist and did empirical work in physics [4]. This integration of a phenomenological and hermeneutical aspect at the foundation of his semiotic view of logic and empirical science is possible because of a changed view on reality and science [5]. The talk explains this construction. References 1. K.-O. Apel, Charles Peirce: From Pragmatism to Pragmaticism, Prometheus Books, New York, 1995. 2. S. Brier, Cybersemiotics: why information is not enough, Toronto University Press, 2008/13. 3. S. Brier, \Can Biosemiotics be a \Science" if its Purpose is to be a Bridge between the Natural, Social and Human Sciences?", Progress in Biophysics and Molecular Biology, vol. 119(3), 2015, pp. 576{587. 4. S. Brier, \How to Produce a Transdisciplinary Information Concept for a Universal Theory of Information?", in Information Studies and the Quest for Transdisciplinarity: Unity through Diversity, volume 9, edited by M. Burgin & W. Hofkirchner, World Scientific Series in Information Studies, World Scientic Publishing, Singapore, 2017, pp. 11{58, https://doi.org/10.1142/9789813109001_0002. 5. V. Romanini & E. Fernandez (editors), Peirce and Biosemiotics: A Guess at the Riddle of Life, Biosemiotics series, vol. 1, Springer, 2014.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".