Clues as information, the semiotic gap, and inferential investigative processes, or making a (very small) contribution to the new discipline, Forensic Semiotics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this article, we try to contribute to the new discipline Forensic Semiotics – a discipline introduced by the Canadian polymath Marcel Danesi. We focus on clues as information and criminal investigative processes as inferential. These inferential (and Peircean) processes have a certain complexity consisting of the interrelation between the collateral observations of the investigator, e. g., his background knowledge concerning criminal and technical analysis, the context that the investigator acts within or in relation to (the universe of discourse), e. g., the scene of crime or the criminal law, as well as the clues as information that will cause the inferential processes in the first place. We believe that this focus can tell us something about crime solving that is not just sensitive to epistemological factors (how to know), but also ontological (what to know) and normative factors as well (how to value the processes of crime solving).
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.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it