Home is Where the Heart Is: Work-from-Home Prevalence and Community Engagement with Nonprofits (WITHDRAWN)
Bibliographic record
Abstract
At a time when social capital and social fabric are eroding in local communities, could the tremendous growth in work-from-home (WFH) practices in recent years be a game changer for community engagement? We address this question by examining the relationship between the county-level prevalence of WFH and community engagement in local nonprofit organizations. Analyzing a comprehensive dataset that combines annual Census data with 1.9 million IRS Form 990 e-filings by 291,032 charitable organizations between 2011 and 2022, we find strong and consistent evidence that nonprofit organizations in counties with higher percentages of remote workers experience both increased charitable donations and higher volunteer participation. Additional analyses reveal that the impact of WFH varies significantly across nonprofit sub-sectors and the organizations’ reliance on donor versus government funding. Our findings suggest that remote work arrangements can strengthen community bonds through increased charitable giving and volunteering. However, these benefits vary across the nonprofit sector, highlighting the need for tailored political and organizational strategies to boost community engagement in an increasingly digital workplace.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".