Let's Do Lunch: Work-related Outcomes of a Food Event
Bibliographic record
Abstract
We all had the experience of sharing meals with coworkers and managers at workplace events. But why is food served during those events? This study aims to examine the effects of food on employees during workplace events. This study built on the impression management framework and the mealtime conversation to investigate the effects of organizational food events on employees’ work engagement levels and affective emotions. The interviews with nine female participants in Canada showed that employees did not focus on work during organizational events after the pandemic. However, sharing food with coworkers left favorable emotions in employees, and stronger ties were built during those workplace events. The results pushed us to explore the effects of workplace events on employees’ attentional focus and energy. Building on the attention restoration theory, this study investigated the effects of food and the different types of interactions during workplace events, work-related versus non-work-related interactions, on employees’ attentional focus and their cognitive, emotional, prosocial, and physical energy. The 133 surveys have shown that employees’ soft attentional focus was stimulated during work-related events compared to non-work-related events. The hard attentional focus was linked to higher reports of prosocial energy. Furthermore, the results also showed that food served during workplace events restored employees’ cognitive, emotional, and prosocial energy. Our study contributed to the understanding of the reason behind serving food during workplace events and demonstrated that corporate events provide an opportunity for employees to build stronger relationships with their coworkers and restore their cognitive, emotional, and prosocial energy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".