Ethical Futures in Qualitative Research: Decolonizing the Politics of Knowledge
Bibliographic record
Abstract
* 1. Introduction: Ethical Futures in Qualitative ResearchNorman K. Denzin and Michael D. Giardina, University of Illinois* 2. The pressing need for ethical educationThomas Schwandt, University of Illinois* 3. Challenges in ethical research practice in a context of global diversityCliff Christians, University of Illinois* 4. Research Regulation, Marginalized Peoples, and Constructing Ethical SystemsYvonna S. Lincoln, Texas A&M University, and Gaile S. Cannella, Arizona State University* 5. The Global Challenge: Research Ethics for Protecting Indigenous KnowledgeMarie Battiste, University of Saskatchewan* 6. Participatory Action Research, Critical Methods, and Indigenous KnowledgesMichelle Fine, Eve Tuck, and Sarach Zeller-Berkman, City University of New York Graduate Center* 7. Red Pedagogy and Qualitative InquirySandy Grande, Connecticut College* 8. Globalization, Resistance, and Qualitative ResearchCorrine Glesne, Independent Scholar* 9. Performing EthicsRonald Pelias, Southern Illinois University* 10. Toward an Ethics of MemoryArthur Bochner, University of South Florida* 11. Relational Ethics in Research with Intimate OthersCarolyn Ellis, University of South Florida* Coda: A Conversation on Qualitative Research Now and in the FutureArthur Bochner, University of South FloridaNorman K. Denzin, University of IllinoisCarolyn Ellis, University of South FloridaYvonna S. Lincoln, Texas A&M UniversityJan Morse, University of AlbertaRonald Pelias, Southern Illinois UniversityLaurel Richardson, The Ohio State University
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 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 | 0.121 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.050 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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.
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".