The Corporate Bullshit Receptivity Scale - Study 2
Bibliographic record
Abstract
This research project aims to create and validate a psychometric tool to measure receptivity to corporate bullshit (i.e., a "Corporate Bullshit Receptivity Scale"). Data for this project was collected previously (May 2024), and a preliminary scale was developed. However, the sample used for this early version was recruited from the general public of Canadian voters for the purposes of a survey on Canadian voter attitudes. There was no requirement that participants either be currently employed or have ever been employed at all; only that they were a Canadian citizen of voting age. Given that the proposed scale is intended to measure receptivity to a certain kind of workplace speech ("corporate bullshit"), thus is specifically contextualized to workplace environments and communication among workers, supervisors, etc., the previous sample was deemed inappropriate for the development of the scale. Therefore, a new sample was recruited from a population of actual workers (with at least 1 year of work experience) from the US and Canada, and this data was used to construct a new version of the CBSR. This resulted in small but potentially meaningful differences in the content of the new scale compared to the previous version. As such, data and results using the previous version of the scale have been abandoned so that the validation process can start over using the new version of the scale. This study will repeat data collection for the convergent and discriminant validity phases of the project using the newer, updated version of the scale.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".