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Record W3014162667 · doi:10.14512/tatup.13.3.32

Policing Science: Genetics, Nanotechnology, Robotics

2004· article· de· W3014162667 on OpenAlexaff
William Leiss

Bibliographic record

VenueTATuP Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis · 2004
Typearticle
Languagede
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRoboticsArtificial intelligenceNanotechnologyEngineeringCognitive scienceComputer sciencePsychologyRobotMaterials science

Abstract

fetched live from OpenAlex

The paper opens with the question raised by Grundmann and Stehr, as to whether "knowledge policy" may include "the aim of limiting, directing into certain paths, or forbidding the application and further development of knowledge". It then explores this theme with reference to contemporary developments in biotechnology and nanotechnology, where the objective of knowledge is to enable us to create and modify at will biological entities (including humans and combined species known as "chimeras"), as well as self-assembling mechanical entities, ab initio through recombinant DNA techniques. I argue that a new category of risks is created by the promised technological applications of these forms of knowledge, called "moral risks", which threatens the ethical basis of human civilization; these are also "catastrophic risks", in that their negative and evil aspects are virtually unlimited. The paper asks whether our institutional structures, including international conventions, are robust enough to be able to contain such risks within acceptable limits; or alternatively whether these risks themselves should be regarded as unacceptable, a position which would impel us to seek to forbid individuals and nations from acquiring and disseminating the knowledge upon which those technologies are based.

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 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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.010
Science and technology studies0.0020.008
Scholarly communication0.0010.002
Open science0.0080.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.014
GPT teacher head0.288
Teacher spread0.274 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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