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Record W2024616867 · doi:10.1525/pol.2007.30.2.210

Research and Moral Law: Ethics and the Social Science Research Relation

2007· article· en· W2024616867 on OpenAlexaboutno aff
Amy Swiffen

Bibliographic record

VenuePoLAR Political and Legal Anthropology Review · 2007
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
Fundersnot available
KeywordsHarmMeta-ethicsInformation ethicsResearch ethicsCriticismApplied ethicsRelation (database)Ethics of technologyContext (archaeology)Action (physics)Engineering ethicsSociologyNormative ethicsEpistemologyPolitical scienceLawEnvironmental ethicsPhilosophyComputer scienceEngineering

Abstract

fetched live from OpenAlex

This paper explores the ethics of social science research by taking the Canadian context as a case study of the increasing formalization of ethics review procedures in North America. Based on a biomedical model of harm prevention, all university research involving humans in Canada, regardless of discipline, must pass through an ethics board review. I read the official ethics policy document governing review procedures for human research in Canada and use two examples of criticism of such policy as entry points to identify and explore a limit in understandings of social research ethics. This limit is reached when ethics policy is criticized on the basis of the incompatibility of a general rule applied to a particular research situation. Using concepts from the ethical philosophies of Kant and Lacan, I move beyond the question of the application of general rules to particular research situations and push research ethics into different territory, where neither general rules nor the notion of particularity can be relied on to ground ethical action. In this other terrain, radical responsibility and unguaranteed decision are the only signposts.

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)0.101
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
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 score0.884
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1010.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.101
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.011
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 head0.683
GPT teacher head0.698
Teacher spread0.015 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

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