Bibliographic record
Abstract
In the struggle for existence, any known species of organisms must avert danger to ensure survival. To do that, they need to identify—or model—it in ways that are specific to their species and actualized in concrete situations. This is an act of generating meaning that falls perfectly within the purview of biosemiotics. Inspired by the most recent pioneering work by Marcel Danesi on the semiotics of danger, the present chapter uses human danger modeling as a case in point to discuss meaning-generation in the biosemiotic context. The rationale is a three-fold one. Firstly, the study of danger, understood in terms of meaning-generation, serves as a fine example of semiotic analysis, because warning signs are among the most primordial and relevant vehicles for meaning. Secondly, the Sebeokian concept of modeling serves as a useful tool to tackle this problem. The reason is that the concept underscores semiotic agency and promises to be a highly integrative framework for studying meaning-generation. Thirdly, humans have evolved into such complex superorganisms that we not only live by existential modeling and semiotic modeling at the same time but are also deeply entangled in our own semiotic webs while probing into and creating possibilities of new meanings. This threefold rationale underlines the need for a comprehensive approach that considers meaning-generation as actualized in different dimensions. This chapter identifies three such dimensions, namely, the representational, interpretational, and existential dimension. These three dimensions form a system that corresponds roughly to the Peircean triad of semiosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".