Beyond numbers: How investment managers accommodate societal issues in financial decisions
Bibliographic record
Abstract
Investment managers use financial numbers to assess the quality of their portfolios, which requires them to estimate the market value of their assets-i.e., the priced exchange for which such assets could be traded. Prior research has shown that investment managers are likely to disregard information that does not easily integrate into such analysis, such as environmental, social and governance (ESG) criteria. We undertook a three-year ethnography of an asset management company to better understand how investment managers respond to ESG criteria. We found that fixed-income investment managers attempted to include ESG criteria in their financial models by financializing the data, so that the information commensurated with their existing models. Equity investment managers, on the other hand, did not financialize ESG issues, but introduced the use of visuals, specifically emojis, to incarnate ESG issues, so that the equity managers could juxtapose ESG criteria with financial criteria. In doing so, they created a sense of dissonance between financial numbers and the visuals, which fostered creative friction. The equity managers were thus able to analyze the ESG criteria not only for their financial insights but also to retain some of the social and environmental information that could not be financialized. We discuss the implications of these findings for the research on financialization and calculative devices.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".