Performing intersectional identity work over time: the historic case of Viola Turner
Bibliographic record
Abstract
Purpose This study aims to analyze the exemplary historic case of Ms Viola Turner – an African-American insurance executive in the early 1900s to gain insights into how individuals negotiate the tension between intersecting identities and moral foundational values over time. Design/methodology/approach This study uses a mixed research design and a genealogical-pragmatic approach to analyze this exemplary case. This study uses computer-aided textual analysis software to analyze interviews conducted with Ms Turner, generating quantitative insights. This study qualitatively codes the interviews to aid in establishing the behavioral patterns across Ms Turner’s lifespan. Findings This study found that Ms Turner altered her underlying configurations of moral foundations to better align with her intersecting identities. This study also revealed cross-level interactions of intersecting identities, life stages and social contexts. Individuals manage and cope with power imbalances through these identity-value alignments. Originality/value The findings shed light on how intersectional history contributes to understanding the ways in which individuals deal with power relationships embedded in intersecting identities over time.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.035 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".