Bibliographic record
Abstract
In contemporary organizations, people are increasingly faced with high work demands, long work hours, and strong time pressure, all of which present challenges for people’s use and management of their energy. To better understand human energy in work organizations and its implications, we need to examine human energy issues in the everyday-that is, human energy is often influenced by practices, activities, experiences, and small actions that would seem rather ordinary and mundane in people’s routine work and life (e.g., sleep patterns, recovery activities at or off work, small moments during the workday). While seemingly small, these factors can be powerful in shaping workplace outcomes through their constant, recurring effects. In the field’s current conversation about human energy in organizations, important questions remain regarding the potency and nuances of the everyday. Thus, this symposium aims to stimulate a scholarly conversation and move forward our understanding of human energy in organizations using this lens of the everyday. Employing a diverse set of methods and contexts, papers in this symposium reveal how energy-related activities, practices, experiences, and small actions in people’s routine work and life (e.g., sleep, recovery activities, workday moments) may involve unique individual choices, shape individuals’ experiences and behavior at work, and even influence workplace outcomes that do not appear to directly relate to energy. In line with this year’s Academy Meeting theme “Making Organizations Meaningful”, this symposium also highlights a purpose of organizations that goes beyond pursuing economic profit and lies in constructing positive human experiences and fulfilling human capacity potential in the workplace. Reenergizing through Work Recovery: Predictors and Results of Recovery Profiles Presenter: John P. Trougakos; U. of Toronto Presenter: Allison S. Gabriel; U. of Arizona Presenter: Andrew A. Bennett; Old Dominion U. Presenter: Jason Dahling; College of New Jersey Presenter: Charles Calderwood; Virginia Commonwealth U. Sleepy First Impressions: Lack of Sleep and Development of Leader-Follower Relationships over Time Presenter: Cristiano L. Guarana; U. of Virginia Presenter: Christopher M. Barnes; U. of Washington Mindful Moments: Dynamic Fluctuations in Meaningfulness and Employee Deviance Behavior Presenter: Erin D. Cooke Long; U. of North Carolina, Chapel Hill Presenter: Michael S Christian; U. of North Carolina, Chapel Hill Presenter: Jocelyn Alisa Lã; U. of North Carolina, Chapel Hill
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.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".