MétaCan
Menu
Back to cohort
Record W3108861017 · doi:10.1177/0033294120978162

This Place Is Full of It: Towards an Organizational Bullshit Perception Scale

2020· article· en· W3108861017 on OpenAlexaff
Caitlin Ferreira, David R. Hannah, Ian P. McCarthy, Leyland Pitt, Sarah Lord Ferguson

Bibliographic record

VenuePsychological Reports · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOperationalizationPsychologyPerceptionScale (ratio)Social psychologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

This study evaluated the psychometric properties of the Organizational Bullshit Perception Scale (OBPS) using two samples of employees of organizations in various sectors. The scale is designed to gauge perceptions of the extent of organizational bullshit that exists in a workplace, where bullshit is operationalized as individuals within an organization making statements with no regard for the truth. Analyses revealed three factors of organizational bullshit, termed regard for truth, the boss and bullshit language. The three factors are consistent with existing literature in the field of organizational bullshit and offer further insight into how employees view workplace bullshit. The OBPS constitutes three subscales measuring these factors. Future researchers should seek to validate the OBPS and further develop the identified factors of organizational bullshit.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.068
GPT teacher head0.379
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePsychological ReportsSame topicMisinformation and Its ImpactsFrench-language works237,207