Bibliographic record
Abstract
Purpose The purpose of this paper is to review the literature on intersectionality and ascertain its potential for application to human resources (HR) research and practice. Particular attention is paid to its methodological issues involving how best to incorporate intersectionality into research designs, and its data issues involving the “curse of dimensionality” where there are too few observations in most datasets to deal with multiple intersecting categories. Design/methodology/approach The methodology involves reviewing the literature on intersectionality in its various dimensions: its conceptual underpinnings and meanings; its evolution as a concept; its application in various areas; its relationship to gender-based analysis plus (GBA+); its methodological issues and data requirements; its relationship to theory and qualitative as well as quantitative lines of research; and its potential applicability to research and practice in HR. Findings Intersectionality deals with how interdependent categories such as race, gender and disability intersect to affect outcomes. It is not how each of these factors has an independent or additive effect; rather, it is how they combine together in an interlocking fashion to have an interactive effect that is different from the sum of their individual effects. This gives rise to methodological and data complications that are outlined. Ways in which these complications have been dealt with in the literature are outlined, including interaction effects, separate equations for key groups, reducing data requirements, qualitative analysis and machine learning with Big Data. Research limitations/implications Intersectionality has not been dealt with in HR research or practice. In other fields, it tends to be dealt with only in a conceptual/theoretical fashion or qualitatively, likely reflecting the difficulties of applying it to quantitative research. Practical implications The wide gap between the theoretical concept of intersectionality and its practical application for purposes of prediction as well as causal analysis is outlined. Trade-offs are invariably involved in applying intersectionality to HR issues. Practical steps for dealing with those trade-offs in the quantitative analyses of HR issues are outlined. Social implications Intersectionality draws attention to the intersecting nature of multiple disadvantages or vulnerability. It highlights how they interact in a multiplicative and not simply additive fashion to affect various outcomes of individual and social importance. Originality/value To the best of the author’s knowledge, this is the first analysis of the potential applicability of the concept of intersectionality to research and practice in HR. It has obvious relevance for ascertaining intersectional categories as predictors and causal determinants of important outcomes in HR, especially given the growing availability of large personnel and digital datasets.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".