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

2017· book· en· W2608339182 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.004
Research integrity0.0010.001
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 head0.023
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