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Record W4205844656 · doi:10.1002/cpz1.354

Ethical, Legal, and Social Implications (ELSI) Research: Methods and Approaches

2022· article· en· W4205844656 on OpenAlexaff
Ubaka Ogbogu, Natasha Ahmed

Bibliographic record

VenueCurrent Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEngineering ethicsField (mathematics)Ethical issuesPoint (geometry)SociologyPolitical scienceManagement scienceEngineering

Abstract

fetched live from OpenAlex

The article provides an overview of select methodologies that are commonly used in ELSI ("ethical, legal, and social implications") research. ELSI is a field that focuses on the analysis of the societal implications of cutting-edge biomedical research and technologies. The article aims to provide an accessible reference on well-established research methods that aspiring and seasoned ELSI researchers can rely on as a starting point for exploring how to design and conduct ELSI studies. © 2022 Wiley Periodicals LLC.

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)0.207
metaresearch head score (Gemma)0.148
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.207
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2070.148
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.009
Science and technology studies0.0090.029
Scholarly communication0.0140.011
Open science0.0040.016
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0130.003

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 head0.607
GPT teacher head0.603
Teacher spread0.004 · 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
GenreMethods

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

Citations16
Published2022
Admission routes1
Has abstractyes

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Same venueCurrent ProtocolsSame topicBiomedical Ethics and RegulationFrench-language works237,207