Clues as information, the semiotic gap, and inferential investigative processes, or making a (very small) contribution to the new discipline, Forensic Semiotics
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.064 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".