Poetry in the boardroom: thinking beyond the facts
Bibliographic record
Abstract
Purpose To convey the findings of an investigation into the relationship between poetry and business thinking, which began with the hypothesis that regular reading and analysis of poetry and its levels of meaning, subtle verbal and nonverbal contextual nuances, emotional content, and required associative thinking will help people deal with ambiguity, delay closure on decisions, and result in more systemic thinking and in better business decisions. Findings The research and workshops indicate that reading poetry can expand thinking space by enhancing associative thinking and access to preconceptual areas. Research limitation/implications The findings are based on extensive interdisciplinary research and a small number of seminars and workshops. No formal studies have yet been conducted. Practical implications This provides a way to open thinking spaces that may be often unused by the business strategist, and that can lead to better decisions. By focusing on how executives can refine their thinking abilities to take them beyond the ordinary limits of cause-and-effect approaches, encourages the application of those radical judgments that can help differentiate one organization from another. Originality/value The authors believe they are the first to explore this relationship between reading poetry and business thinking.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".