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Record W2565216370 · doi:10.1002/pra2.2016.14505301026

Ethical tensions in research: The influence of metatheoretical orientation on research ethics

2016· article· en· W2565216370 on OpenAlexaff
Sarah Barriage, Wayne Buente, Elke Greifeneder, Devon Greyson, Vanessa Kitzie, Miraida Morales, Ross J. Todd

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsChild and Family Research Institute
Fundersnot available
KeywordsEngineering ethicsNegotiationResearch ethicsInformed consentContext (archaeology)Ethical issuesInstitutional review boardEthical codePolitical sciencePublic relationsSociologyPsychologySocial scienceMedicineEngineering

Abstract

fetched live from OpenAlex

ABSTRACT When developing one's own research agenda, early and mid‐career researchers continually negotiate how best to meet ethical standards and resolve ethical constraints using methodologically sound approaches. Often such struggles occur behind closed doors, their outcomes reflected in the institutional language of an ethical review board. This panel seeks to bring these struggles to the forefront by having panelists who study various populations discuss how they approach ethical challenges in their research. Due to the nature of the groups these panelists study, the panel provides a context where the site of ethical struggles, challenges, and tensions are exacerbated. Key issues to be discussed are: informed consent, risks to participants, and research design and dissemination. Discussion of these issues will be oriented around each participant's metatheoretical orientation to research in library and information science (LIS). Adopting such an approach will highlight some of the main challenges when engaging in ethical practices that may not align with institutional standards, as well as denote possible strategies for addressing them.

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)0.159
metaresearch head score (Gemma)0.403
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
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: Empirical
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1590.403
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0020.011
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.362
GPT teacher head0.587
Teacher spread0.225 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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