Dealing with the Social Media Polycontextuality of Work
Bibliographic record
Abstract
Practice and Policy Oriented Abstract This article views social media for work not only as technologies that enable people to do certain things, but also as contexts with emerging norms and roles in which people participated. As they do so, people are confronted with opportunities and challenges that are inherent to social media polycontextuality, that is, with multiple social media–based contexts of relevance to work. This study offers guidance for people on how their participation in multiple social media contexts affects their work positively and negatively and how they can manage the associated opportunities and challenges. It also reveals how people’s engagement with social media polycontextuality may change as their employment status and work experiences evolve. Moreover, this study holds managerial implications by bringing awareness to how employees’ participation in social media contexts bypasses the organization and, thus, their typical purview but is still associated with work rather than leisure. Managers can understand better their employees’ situations and examine how social media contexts affect them within and beyond organizational boundaries and shape what they can or cannot do in their work. A better understanding of social media polycontextuality also brings managers new insights to communicate with employees.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| 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".