When Aging and Climate Change Are Brought Together: Fossil Fuel Divestment and a Changing Dispositive of Security
Bibliographic record
Abstract
Pension funds have become major targets for the incorporation of climate change into their investment decisions. Recently, divestment from carbon intensive companies or industries has been the object of a wave of campaigns directed at these institutional investors. This paper uses Foucault’s dispositive of security to investigate the decisions of one organization, the New York State Common Retirement Fund, which in 2021 divested from seven oil sands companies. Conceptualizing divestment within a security dispositive helps us build theory which understands divestment within existing security-oriented arrangements. It shows how changes build upon the existing dispositive, and that by looking to existing governing arrangements we can see elements that act as operators to change their direction and emphasis. In the case of pension fund divestment, risk is the operator that both sustains the investment function and also tilts the arrangement towards climate change. In these existing arrangements lay the ingredients for future social 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 | 0.001 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".