Distances and directions: An emotional journey into the recovery process.
Bibliographic record
Abstract
Positive emotions stemming from leisure activities are often promoted as a way to achieve a state of recovery, in particular by counteracting negative emotions experienced throughout the workday. Yet the recovery literature frequently takes an undifferentiated view of both the positive emotions employees experience as well as the negative emotions employees are recovering from. This implicitly assumes that all positive emotions are equally effective in facilitating recovery from all negative emotions. Drawing from theory treating emotional movements as a metaphorical journey, we develop a framework for understanding recovery that highlights the importance of the distance and direction that individuals "travel" when moving from negative emotions to positive emotions during the recovery process. We argue that the negative emotions that people start with from work-that is, their emotional origin-as well as the positive emotions that people end with following leisure activities-that is, their emotional destination-jointly influence the state of being recovered. Across two studies using experience-sampling methodologies, we find that "shorter" journeys consisting of emotional destinations that match the activation level of emotional origins (e.g., experiencing high activation positive emotion [HAP] to counter high activation negative emotion) are effective in promoting recovery, while "longer" journeys consisting of mismatches (e.g., experiencing HAP to counter low activation negative emotion) are ineffective for recovery. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".