Peircean anti-psychologism and learning theory
Bibliographic record
Abstract
Abstract Taking influence from Peirce’s phenomenological categories (Firstness, Secondness, Thirdness), a notion of what we call bottom-up modeling has become increasingly significant in research areas interested in learning, cognition, and development. Here, following a particular reading of Peircean semiotics (cf. Deacon, Terrence. 1997. The symbolic species: The co-evolution of language and the brain. London and New York: W. W. Norton; Sebeok, Thomas and Marcel Danesi. 2000. The forms of meaning: Modelling systems theory and semiotic analysis. Berlin and New York: Mouton de Gruyter), modeling, and thus also learning, has mostly been thought of as ascending from simple, basic sign types to complex ones (iconic – indexical – symbolic; Firstness – Secondness – Thirdness). This constitutes the basis of most currently accepted (neo-Peircean) semiotic modeling theories and entails the further acceptance of an unexamined a priori coherence between complexity of cognition and complexity of signification. Following recent readings of Peirce’s post-1900 semiotic, we will present, in abbreviated form, a discussion as to the limits of this theoretical approach for theories of learning that draws upon Peirce’s late semiotic philosophy, in particular his late work on iconicity and propositions. We also explore the corollary conceptions of semiotic resources and competences and affordances to develop an ecological perspective on learning that notably does not impose a linear developmental progression from simple to complex. In conclusion, we address some of the implications of this (post-Peircean) conceptualization for transdisciplinary research into learning.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.053 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".