Exercising Judgment in Organizations
Bibliographic record
Abstract
Judgment is a fundamental concept in management research and relates to several subfields, ranging from human resources (Grandey, Houston & Avery, 2019) and entrepreneurship (Foss & Klein, 2012; Foss, Klein, & Bjørnskov, 2019) to strategic decision-making (Priem, 1994) and business ethics (Mudrack & Mason, 2013). The concept's popularity has resulted in a diversity of understandings and applications – some emphasizing the technical aspects of judgment like precision and accuracy, while others more concerned about judgment as a skillful practice (Tsoukas, Hadjimichael, Nair, Pyrko, & Woolley, 2024). At the same time, judgment is critical for navigating contemporary issues, such as developing leadership traits and character (Crossan, Crossan, Newstead, & Sturm, 2024), evaluating the role of AI in everyday work (Lebovitz, Lifshitz-Assaf, & Levina, 2022), entrepreneurial decision making under conditions of uncertainty and unknowingness (Shepherd, Williams & Patzelt, 2015) and understanding how managers form views and interpret ambiguous evidence in a way that will lead to a good decision (Likierman, 2020) – especially in light of the need to address wicked problems and grand societal challenges (Ackermann, Pyrko, & Hill, 2024). To this end, this symposium aims to focus scholarly attention on the role of judgment in business and management, reflect on its characteristics in a fast-changing world, and discuss the implications and future research directions for judgment as an area of study in business and management research.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| 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".