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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it