If Adorno Met Intersectionality Theory: Reconceiving the Method of Negative Case Analysis
Bibliographic record
Abstract
This article endeavors to present a methodological innovation that is described here as Adornian negative case analysis (ANCA). The method of negative case analysis is theoretically expanded upon using Adornian and Intersectional lenses and in doing so, it provides a means by which equity, diversity, and inclusion (EDI) can be substantiated in research design. ANCA is inclusive of underrepresented perspectives so that points of comparison can be created with the intent of challenging assumptions that have historically negated the experiences of diverse and equity-seeking groups. Understanding that negative case data require saturation, identities can be appreciated as irreducibly complex, but subsequently explored in relation to the universalizing social categories that work to sustain imbalanced power relations. To demonstrate how ANCA can be practically applied, the author will describe how it was used in her doctoral research study. Beginning with recruitment and following through to data analysis, this article provides a summary of how the substantiation of EDI in the design of a Constructivist Grounded Theory study transpired through ANCA.
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 | 0.222 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.053 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".