The social process of coping with work‐related stressors online: A machine learning and interpretive data science approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract People are increasingly turning to social media and online forums like Reddit to cope with work‐related concerns. Previous research suggests that how others respond can be an important determinant of the sharer's affective and well‐being outcomes. However, less is known about whether and how cues embedded in the content of what is shared can shape the type of responses that one receives from others, obscuring the joint and interactive role that both the sharer and listener may play in influencing the sharer's outcomes. In this study, we develop theory to advance our understanding of online coping with an explicitly social focus using computational grounded theorizing and machine learning (ML) techniques applied to a large corpus of work‐related conversations on Reddit. Specifically, our theoretical model sheds light on the dynamics of the online social coping process related to the domain of work. We show that how sharers and listeners interact and react to one another depends on the content of stressors shared, the social coping behaviors used when sharing, and whether the sharer and listener belong to the same occupational context. We contribute to the social coping literature in three ways. First, we clarify how social actors respond to cues embedded in the social coping attempt. Second, we examine the moderating role that such responses play in shaping sharer outcomes. Finally, we extend theory on social coping with work‐related stressors to the online domain. Taken together, this research highlights the importance of the dynamic interplay between sharer and listener in the context of online social coping.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it