Bibliographic record
Abstract
This article focuses on geographic communities as fields in which human-made and natural events occasionally disrupt the lives of organizations. We develop an institutional perspective to unpack how and why major events within communities affect organizations in the context of corporate philanthropy. To test this framework, we examine how different types of mega-events (the Olympics, the Super Bowl, political conventions) and natural disasters (such as floods and hurricanes) affected the philanthropic spending of locally headquartered Fortune 1000 firms between 1980 and 2006. Results show that philanthropic spending fluctuated dramatically as mega-events generally led to a punctuated increase in otherwise relatively stable patterns of giving by local corporations. The impact of natural disasters depended on the severity of damage: while major disasters had a negative effect, smaller-scale disasters had a positive impact. Firms’ philanthropic history and communities’ intercorporate network cohesion moderated some of these effects. This study extends the institutional and community literatures by illuminating the geographic distribution of punctuating events as a central mechanism for community influences on organizations, shedding new light on the temporal dynamics of both endogenous and exogenous punctuating events and providing a more nuanced understanding of corporate-community relations.
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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 | 0.004 | 0.013 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".