Bibliographic record
Abstract
If one thing has been made clear in the events of 2020, it is that organizations, teams, and individuals must continually adapt to change. In other words, the need for continual learning is in high demand. This session will focus on the understudied role of the manager in leading employee learning. In the first presentation, Rigolizzo, Zhu & Cruz evaluate the role that perceptions of school have on employees’ motivation to engage in learning behaviors. Traditionally, when managers consider employees’ time in school, they focus solely on the degree achieved and the content of the learning. However, this empirical study demonstrates that, in school, individuals develop attitudes towards learning that influence their behavior in learning tasks years later. The second presentation provides new insight into the managerial factors that impact effective team feedback. Team feedback research has traditionally focused on feedback valence as a predictor of team outcomes, though with inconclusive and contradictory findings. In this paper, we propose that the informational content of the feedback may play a vital role in the effect of constructive feedback on teams. Finally, Kayes & Bürgi-Tian will present a paper that explores learning-based experiences, which are attitudes that individual employees associate with progress and frustration in learning situations. We use qualitative and quantitative data on learning-related experience ‘episodes’ to provide a better understanding of experiences that are associated with progress on learning and frustration with learning to provide insights into how to sustain learning and improvement efforts. Leading Employee Learning Overview Presenter: Michele Rigolizzo; Montclair State U. The Lasting Effects of School Experiences: How perceptions of school impact managers’ learning Presenter: Michele Rigolizzo; Montclair State U. Presenter: Zhu Zhu; Montclair State U. Presenter: Adrianna Cruz; Montclair State U. Constructive Feedback: When Leader Agreeableness Stifles Team Information Processing Presenter: Jean-François Harvey; HEC Montreal Presenter: Paul Isaac Green; U. of Texas at Austin Learning-based Experiences at Work: How Leaders can Enhance Employee Learning Presenter: D Christopher Kayes; George Washington U. Presenter: Jing Tian; George Washington U.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.149 | 0.042 |
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".