Construal Level in Organizational Research
Bibliographic record
Abstract
Every day people encounter situations at work with varying demands. How do people navigate changing demands across different situations? Construal level theory argues that people use abstract construals to address distant demands and concrete construals to respond to immediate demands. Decades of psychology research have shown the value of construal level theory in explaining and predicting people’s attitudes and behaviors. More recent research has applied construal level to studying organizational-relevant phenomena. This symposium presents five lines of research that use diverse methodologies and samples to explore the antecedents and consequences of communication abstraction and cognitive construal, including gender, audience engagement, performance, and information processing and trust in groups. This symposium aims to provide an opportunity for knowledge sharing and discussion among researchers who are interested in construal level in organizational research. Gender and Emoji Usage Presenter: Gil Appel; George Washington U. Presenter: Cheryl Wakslak; U. of Southern California Presenter: Elinor Amit; Tel Aviv U. Inviting People In: Does Abstract Language Increase Engagement with Ideas? Presenter: Jean-Nicolas Reyt; McGill U. Presenter: Patricia Staats; Kenan-Flagler Business School, U. of North Carolina at Chapel Hill Presenter: Naomi Beth Rothman; Lehigh U. Construal of Everyday Tasks Presenter: Yidan Yin; U. of California, San Diego Presenter: Pamela K. Smith; U. of California, San Diego Presenter: Batia Mishan Wiesenfeld; New York U. Developing Measures for Abstract Construal and Concrete Construal Presenter: Robert Barrett; U. of Iowa Presenter: Yidan Yin; U. of California, San Diego Presenter: Michele Williams; U. of Iowa Presenter: Batia Mishan Wiesenfeld; New York U. Preventing Groupthink through a Concrete Construal Intervention Presenter: Ashli Carter; NYU Stern School of Business
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".