Bibliographic record
Abstract
Abstract This paper provides an integrative and updated view of modeling in semiotics. It postulates that the essence of modeling is supersession. In any act or instance of modeling, the model supersedes and is brought to the front for salience, accessibility, and operability, whereas at the same time the modeled recedes and exists in the background, inaccessible and inoperable. The paper goes on to differentiate between two major types of modeling, the underlying “existential modeling,” functioning as the fundamental scaffold and the genuine foundation of all other types of modeling as we know them, and the overlaying “semiotic modeling,” designating the process of creation and use of “forms of meaning,” a process that underlies both cognition and communication. By focusing on semiotic modeling, the paper features an unconventional view that casts a new light on the relation between a model and a sign and thus the relation between semiotic modeling and semiosis. Endorsing an embodied approach to meaning-making as semiotic modeling, the paper finds it important to stress the appropriateness and necessity of understanding the term “model” as a verb rather than as a noun, in that modeling is never static and should be properly regarded in terms of embodied action.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".