The Corporate Bullshit Receptivity Scale: Development, validation, and associations with workplace outcomes
Bibliographic record
Abstract
From boardrooms and brown bags, to emails and earnings calls, business culture often seems overrun by “corporate bullshit,” a type of semantically empty or otherwise vague rhetoric that leverages abstruse corporate buzzwords and jargon in a way that misrepresents or obscures some aspect of organizational reality. Though corporate bullshit may sometimes seem harmless, it can disrupt organizational and employee effectiveness in numerous ways including obstructing effective communication, increasing employee disengagement, tarnishing company reputation, and exposing businesses to legitimate financial and legal risks. Here, results from three studies (N = 745) report the construction and validation of the Corporate Bullshit Receptivity Scale (CBSR), a novel measure of individual differences in receptivity to corporate bullshit. Results show that corporate bullshit receptivity is distinct from a general affinity for corporate speech. Moreover, it is significantly associated with measures of analytic thinking and other bullshit-related constructs in theoretically-consistent ways. Importantly, corporate bullshit receptivity is strongly associated with several measures of organizational culture and job performance and is a strong, robust predictor of work-related decision-making. Overall, the findings establish the CBSR as a valid and reliable tool to aid researchers in examining the causes, correlates, and consequences of bullshit in the workplace.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".