In the Eye of the Beholder: Advancing Feedback Research with a Focus on Perceptions
Bibliographic record
Abstract
In this symposium, we advance feedback research by considering how perceptions guide effective feedback interactions. By perceptions, we mean how providers and recipients view one another and their relationship, as well as how they view the feedback being relayed and tasks being evaluated. This symposium brings together leading scholars to consider the role of perceptions at each stage of a feedback interaction – at the beginning, when givers are considering feedback delivery, in the middle, when it is delivered and interpreted, and after the interaction, when its consequences unfold. In doing so, the symposium examines the implications of feedback interactions across multiple levels, building from the dyadic level, to the group level, crowd level, and ultimately, the societal level, yielding policy-relevant insights. The presentations were also selected to exhibit the breadth of methodologies that are being applied to explore the role of perceptions in feedback interactions. The papers include findings from surveys, archival field data, and experiments. Together, these presentations propose theories and offer practical implications that will advance our understanding of – and insight into how to improve – feedback processes. Mistaking Employee Silence for Satisfaction and Other Manager Misperceptions Author: Erik Santoro; - Author: Frank Flynn; Stanford U. Author: Benoit Monin; Stanford Graduate School of Business Unpacking the Power of Feedback: Investigating the Structure of Effective Feedback Author: Yingyue Luan; Cambridge Judge Business School Author: YeunJoon Kim; U. of Cambridge Author: Myung Chung; Cambridge Judge Business School Interpreter of Maladies: How Feedback Aggregators Interpret Conflicting Feedback Author: Ting Zhang; Harvard Business School Author: Michael White; Columbia Business School Author: Tuna Cem Hayirli; Harvard Business School Candid Disclosure in Team Debriefs Author: Nate Fulham; - Author: Matthew A. Diabes; Carnegie Mellon U. - Tepper School of Business Author: Binyamin Cooper; Morgan State U. Author: Taya R. Cohen; Carnegie Mellon U. - Tepper School of Business The Failure Gap Author: Lauren Eskreis-Winkler; Northwestern Kellogg School of Management Author: Kaitlin Woolley; Cornell SC Johnson College of Business Author: Eliana Polimeni; Northwestern Kellogg School of Management
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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 | 0.048 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.021 | 0.040 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".