Bibliographic record
Abstract
Social work is committed to promoting social justice, inclusion and the empowerment of people. Qualitative research methods offer exciting possibilities for operationalizing this commitment. Drawing predominantly on constructivist and/or critical paradigms for understanding, qualitative research fosters a rebalancing of power within the researcher/researchee relationship and encourages a focus on marginalized understandings and experiences. More than this, it lends itself to an analysis of power. To realize the potential of qualitative research, however, requires more than just developing a knowledge base; it also requires integrating a different way of “being” as a researcher and social worker. Facilitating this learning process with social work students raises interesting challenges and opportunities. The purpose of this paper is to open for discussion ways for teaching qualitative research that allow the iterative, creative, and reflective practices required for effective qualitative research to develop. Drawing on our experiences teaching qualitative research and student feedback accumulated over the past five years, we discuss aspects of the course that have seemingly “worked” and others that have been less effective. The intent is to initiate a discussion around the ethics and pragmatics of teaching qualitative research.
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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.063 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".