Fuzzy-defined entities: A key concept to strengthen forensic science foundations?
Bibliographic record
Abstract
According to the Sydney Declaration, "Forensic science is [… an] endeavour to study traces […] through their detection, recognition, recovery, examination and interpretation to understand anomalous events of public interest (e.g., crimes, security incidents)." This science is focused on establishing the nature and relationships among entities related to events having a potential legal impact. Entities can be (groups of) persons, objects, activities and their corresponding sources, events and traces. Although uniqueness of an entity has been traditionally accepted as a principle of forensic science, this paper argues and illustrates that such uniqueness is illusory: Not only can an entity evolve spatially and temporally, but at any specific instant, it differs from itself according to the level of precision at which it is considered. Its characteristics vary based on when, how and by whom it is perceived. We introduce the concept of fuzzy entities - defined to formally include some essential uncertainty or imprecision. The essential impreciseness and subjectivity of an entity gives a new perspective that allows us to revisit Kirk's principle of individuality and to propose to replace it with a new principle of fuzzy unicity. We believe that this new perspective has the potential to strengthen forensic science foundations and bring closer its disciplines, which is an important step towards a harmonized forensic science.
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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.009 | 0.028 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".