Bibliographic record
Abstract
The concept of resilience has entered into the lexicon of the everyday while enjoying an ever-expanding range of applications and associations within the public, private, and philanthropic sectors. This dissertation critically engages resilience discourses and practices by tracing a network of relations composed of both people and things. Taking cues from Actor-Network Theory, I bring into focus a latticework of actants that has enabled the concept of resilience to function, be mobile, expand and stabilize into what I call 'philanthropic resilience' and 'financialized resilience'. I offer a novel reading of the career of resilience by shedding light on how the concept has been deployed across a diverse set of fields and assemblages. To illustrate key features of the philanthropic form of resilience I turn to the Rockefeller Foundation's 100 Resilient Cities initiative (100RC), to date the most expansive experiment in incubating city resilience interventions. I show how the 100RC successfully grew a resilience network through philanthropic partnerships, how it created the Chief Resilience Officer position as an embedded municipal actor, and how it enrolled a selective set of actants tasked with solving the problem of resilience valuation theoretically and practically. I argue that the idea of 'resilience dividends', once confined to the philanthropic sector, has been incorporated into the financialized resilience form through speculative investment products such as bonds. I contend that resilience bonds, along with the issuance of profit-generating climate resilience bonds, show that the concept of resilience has materialized in powerful social practices beyond metaphorical and metonymical applications. This dissertation offers theoretical and empirical lenses to better parse the political and social ramifications of the business of resilience.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".