Intersectional history: exploring intersectionality over time
Bibliographic record
Abstract
Purpose This paper aims to outline the possibilities of intersectional history as a novel method for management history. Intersectional history combines intersectionality and the study of the past to examine discrimination in organizations over time. This paper explores the need for intersectional work in management history, outlines the vision for intersectional history and provides a brief example analyzing the treatment of Australian Aboriginal people in a historical account of Qantas Airways. Design/methodology/approach This paper contends that intersectionality is a discursive practice, and it adopts a relational approach to the study of the past to inform the method. This paper focuses on the social construction of identities and the enduring nature of traces of the powerful in organizations over time. Findings The example of Qantas Airways demonstrates that intersectional history can be used to interrogate powerful traces of the past to reveal novel insights about marginalized peoples over time. Originality/value Intersectional history is a specific and reflexive method that allows for the surfacing of identity-based marginalization over time. The paper’s concentration on identity as socially constructed allows a particular focus on notions or representations of the marginalized in traces of the past. These traces may otherwise mask the existence and importance of marginalized groups in organizations’ dominant histories.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.014 | 0.055 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".