What We Talk About When We Talk About Inequality: An Introduction to the <i>Journal of Management Studies</i> Special Issue
Bibliographic record
Abstract
Abstract This introduction to the Journal of Management Studies Special Issue on Inequality argues that the way we frame conversations about inequality reveals important information about how poverty and inequality have become institutionalized in modern society. We observe a distinct recent shift in the collective conversation about vulnerable populations in western society away from poverty and toward inequality. We question why this shift has occurred and who benefits from it. Drawing from the provocative papers that populate the Special Issue we describe how forms of talk can help create inequality, maintain it and holds the potential to change it. We encourage new research that adopts a holistic reintegration of poverty and inequality by attending to the ‘dirty realism’ of the violence of poverty and the dire consequences of internalized inequality.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".