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Record W2180891946 · doi:10.1098/rstb.2014.0260

The end of the (forensic science) world as we know it? The example of trace evidence

2015· article· en· W2180891946 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsTRACE (psycholinguistics)Presentation (obstetrics)NormativeEngineering ethicsPolitical scienceSuspectEpistemologyLawEngineeringMedicine

Abstract

fetched live from OpenAlex

The dominant conception of forensic science as a patchwork of disciplines primarily assisting the criminal justice system (i.e. forensics) is in crisis or at least shows a series of anomalies and serious limitations. In recent years, symptoms of the crisis have been discussed in a number of reports by various commentators, without a doubt epitomized by the 2009 report by the US National Academies of Sciences (NAS 2009 Strengthening forensic science in the United States: a path forward). Although needed, but viewed as the solution to these drawbacks, the almost generalized adoption of stricter business models in forensic science casework compounded with ever-increasing normative and compliance processes not only place additional pressures on a discipline that already appears in difficulty, but also induce more fragmentation of the different forensic science tasks, a tenet many times denounced by the same NAS report and other similar reviews. One may ask whether these issues are not simply the result of an unfit paradigm. If this is the case, the current problems faced by forensic science may indicate future significant changes for the discipline. To facilitate broader discussion this presentation focuses on trace evidence, an area that is seminal to forensic science both for epistemological and historical reasons. There is, however, little doubt that this area is currently under siege worldwide. Current and future challenges faced by trace evidence are discussed along with some possible answers. The current situation ultimately presents some significant opportunities to re-invent not only trace evidence but also forensic science. Ultimately, a distinctive, more robust and more reliable science may emerge through rethinking the forensics paradigm built on specialisms, revisiting fundamental forensic science principles and adapting them to the twenty-first century.

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 imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.006
Science and technology studies0.0030.025
Scholarly communication0.0000.000
Open science0.0090.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.351
GPT teacher head0.398
Teacher spread0.047 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it