Income inequality ignored? An agenda for business and strategic organization
Bibliographic record
Abstract
Despite a vibrant body of scholarship and a growing public discourse around the socio-political consequences of rising income inequality around much of the world, very little is known about the organizational consequences of societal-level income inequality. In this essay, we draw upon previous literature on the socio-political consequences of high income inequality to help identify its potential business consequences. In particular, we suggest that high levels of income inequality can give rise to (1) social movements that coerce and constrain firms’ actions, (2) alternative organizational forms that displace existing organizations and (3) new political and regulatory risks that undermine firms’ performance or survival. Using this argument, we emphasize the broader point that income inequality matters to firms and markets and that the study of inequality needs to be ‘brought in from the cold’ by organizational researchers. Furthermore, we outline a specific research agenda aimed at better understanding income inequality and how organizations can respond to it.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".