Learning and knowing as semiosis: Extending the conceptual apparatus of semiotics
Bibliographic record
Abstract
If all knowing comes from semiosis, more concepts should be added to the semiotic toolbox. However, semiotic concepts must be defined via other semiotic concepts. We observe an opportunity to advance the state-of-the-art in semiotics by defining concepts of cognitive processes and phenomena via semiotic terms. In particular, we focus on concepts of relevance for theory of knowledge, such as learning, knowing, affordance, scaffolding, resources, competence, memory, and a few others. For these, we provide preliminary definitions from a semiotic perspective, which also explicates their interrelatedness. Redefining these terms this way helps to avoid both physicalism and psychologism, showcasing the epistemological dimensions of environmental situatedness through the semiotic understanding of organisms’ fittedness with their environments. Following our review and presentation of each concept, we briefly discuss the significance of our embedded redefinitions in contributing to a semiotic theory of knowing that has relevance to both the humanities and the life sciences, while not forgetting their relevance to education and psychology, but also social semiotic and multimodality studies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.042 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".