Bibliographic record
Abstract
This chapter focuses on the potential for DICTION to identify inaptly hubristic language of Chief Executive Officers. CEO hubris is examined as a syndrome possessing identifiable symptoms that have possible links to CEO language and DICTION measures. The authors make some exploratory predictions regarding the nature of these links and assess them using the text of speeches of the former long-serving CEO of British Petroleum, John Browne. In a quest for validation, they then apply the results of that assessment to some oral and written examples of the discourse of News Corporation’s CEO, Rupert Murdoch. The results, although mixed, show some promise regarding the usefulness of DICTION in identifying hubristic CEO-speak. One interesting finding is that DICTION’s calculated variable, Variety, is associated strongly and consistently with the language use of Browne and Murdoch, evidencing a high Type Token Ratio. The authors attribute this result to Browne and Murdoch possibly experiencing low anxiety as they strived to manage impressions of themselves by inducing the outside world to “know” what they were seemingly utterly convinced about - their own superiority. The chapter concludes by suggesting some refinements and extensions of the study.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".