Ethical tensions in research: The influence of metatheoretical orientation on research ethics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.529 | 0.518 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".