The Weakness of Weak Ties: Do Social Capital Investments Among Leaders Pay off During Disasters
Bibliographic record
Abstract
The theoretical literature on social capital and disasters, as well as conventional wisdom, suggests the importance of pre-disaster relationship building among leaders of responding organizations and agencies for disaster readiness and response. Often implied, but rarely tested empirically, research presumes a positive and linear relationship associated with investments in social capital for effective disaster response. Any amount of relationship building is better than none, but more is better. But is it? In this article, we use a rare longitudinal, pre-post disaster dataset of dyadic ties among leaders to examine key questions related to investments in social capital before a disaster, the expected payoffs from these investments, the actual payoffs of these investments and the marginal effects of such investments. Our findings indicate that pre-disaster relationship building has a non-linear relationship to expected payoffs and actual payoffs. Marginal effects analysis suggests three interesting, though perhaps counter-intuitive, relationships between the investment and expected and actual payoffs in social capital. First, leaders reported expecting disproportionately high payoffs from relatively small relationship investments prior to the incident. Second, infrequent pre-disaster interactions were found to be no different than no prior interaction when looking at actual payoffs from these investments. Finally, relationships that were deemed most problematic were among those with weak ties. Overall, results suggest that the efficacy of pre-disaster relationship building is more complicated than one would expect based on extant literature. More investment in social capital may be better in some cases, but the benefits from these investments appear only after a certain threshold is met and, in some cases, may have diminishing returns. Potential theoretical drivers for these seemingly counter-intuitive findings are discussed while calling for further research to investigate these dynamics in other contexts.
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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 | 0.002 | 0.028 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".