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Regulating New Technologies: EU Internal Market Law, Risk, and Socio-Technical Order

2017· book· en· W2608339182 on OpenAlexfundno aff
Mark Flear

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsOrder (exchange)LimitingBioethicsLaw and economicsEmerging technologiesBusinessProduct (mathematics)Political scienceDomestic marketRisk analysis (engineering)EngineeringLawEconomicsComputer science

Abstract

fetched live from OpenAlex

The chapter argues that, more than playing catch up with and being determined by technoscientific innovation, law also plays a leading role in the regulation of new technologies by shaping and directing the conditions of possibility for their development and market availability. The chapter charts some of the main ways in which EU internal market law retains its regulatory capacity and efficacy through techniques of negative and positive integration. These techniques centralize the harms or hazards relating to product safety as ‘the’ risks posed by new technologies. Designing regulation and limiting ‘risk’ (through it) marginalizes and obscures other kinds of harms or hazards to which it might pertain. The current regulatory design also depoliticizes, naturalizes, and quells contestation around the approach taken and obscures other potential framings of regulation, such as by human rights and bioethics.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.022
Scholarly communication0.0140.008
Open science0.0010.004
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.212
Teacher spread0.188 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

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

Citations6
Published2017
Admission routes1
Has abstractyes

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