Self-Isolated but Not Alone: Community Management Work in the Time of a Pandemic
Bibliographic record
Abstract
As the COVID-19 crisis forces individuals to self-isolate, work from home, and find new leisure activities, an increasing number are turning to online gaming. These online communities are often developed by community managers who work to engage communities and establish norms. Community management work, broadly, is considered the “soft-skilled” labor of communication, diplomacy, and empathy within an online community. Despite an obvious need for this work in mediating the myriad of personalities and sheer number of users, community management is often underpaid and precarious. Using early interviews with community managers, conducted during the COVID-19 crisis, I aim to highlight those who work promoting pro-social behavior in leisure spaces online. This work plays a vital role in community well-being, particularly for those who have not previously interacted extensively online. Community management is arguably an essential service during times of self-isolation, as they corral toxicity and shepherd users into positive online communities.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| 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".