Exercising Judgment in Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it