Exploring the Expansive Properties of Interpretive Description: An Invitation to Anti-oppressive Researchers
Bibliographic record
Abstract
There is an ever-present need to challenge, create, and expand upon qualitative research approaches in the applied and practice disciplines to avoid repeating mistakes of the past and to realize a research agenda for socially just practice. Toward these goals, anti-oppressive researchers engage with a variety of methodologies to co-produce accounts that reflect a comprehensive understanding of social problems with the people who experience them and to enact solutions for real world change. In this article, we reflect on the manner in which Interpretive Description may be a useful option for anti-oppressive researchers to consider as a methodological approach in meeting these philosophical and practical aspirations. We find that Interpretive Description offers guidance toward building the foundation, bringing your whole self to the research, remaining responsive to people, valuing people’s expert perspective of their own experience, using power and privilege wisely, broadening contributors and consumers of research, embracing complications and variations, and enacting change. To illustrate this, we share examples from a participatory, anti-oppressive Interpretive Description study conducted by a team comprised of an inter-racial coalition of students, alumni, and faculty. Collectively, we investigated Black graduate student experiences of racism, inclusion, and expansion within a historically and primarily White university. This case example illustrates our contention that, as our commitment to anti-oppressive research and practice in the applied disciplines intensifies, Interpretive Description is well situated to help us advance practice knowledge in a manner that is transparent, equitable and credible.
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 | 0.324 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.026 | 0.248 |
| Scholarly communication | 0.036 | 0.074 |
| Open science | 0.007 | 0.058 |
| Research integrity | 0.021 | 0.044 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".