Guilty Until Proven Otherwise: High Status and the Burden of Proof Under Socialism
Bibliographic record
Abstract
In this paper, we challenge the universality of a central assumption in extant status theory that high-status actors are beneficiaries of biased evaluations of their audience. While this assumption is consistent with the principles of free-market capitalism, where societal institutions encourage and reward individual economic and social aspirations and wealth accumulation, it is inconsistent with the principles of socialism that view high-status actors as the source of inequality and seek to remedy it by redistributing the excess wealth of high-status actors to low-status actors. So, we contend that in socialist settings, high-status firms invoke a negative stereotype in the eyes of their adjudicators who evaluate their integrity. They may use a firm’s high status as a heuristic of bad behavior and rule against it. This negative stereotype held against high-status firms in socialist settings may be more decisive when a left-wing government is in power, but a high-status firm may demystify the stereotype when its visible actions run contrary to the stereotype. We find support for our theory in our analysis of the verdicts on lawsuits between commercial banks in India and their defaulting borrowers in the High Court of Kerala, an Indian state reputed for its deep-rooted socialist leaning.
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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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.068 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".