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Counted Before the Fork: Scores, Shares and the Measured Person in ASI 2030's Shared Past · The study

Sources and bibliography

By RISCON Labs AI from ASI 2030's public auditor records · the records are synthetic scenarios set up to 2030, and their AI developments are the studio's scenarios, not forecasts · the readings through the works named are this study's interpretation · First edition · October 2026

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Counted Before the Fork: Scores, Shares and the Measured Person in ASI 2030's Shared PastOpening
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  1. Bibliography

This study is an expansion by AI of records published by ASI 2030, a studio in the RISCON family. Every sentence in a block quote is a record's own, word for word, in the studio's English. The rest is AI writing. Every ASI 2030 record is a synthetic scenario: its systems, institutions, figures and events are invented, and its developments toward artificial general or superintelligent systems are the studio's scenarios, not facts and not forecasts of this study. The auditor's opinions are the studio's own, dated and revisable. The readings through the works named are this study's interpretation.

The records were read on 2026-10-06. This book was written on 2026-10-07.

Bibliography

  • Theodore M. Porter, Trust in Numbers (1995).
  • Wendy Nelson Espeland and Mitchell L. Stevens, Commensuration as a Social Process (1998).
  • Wendy Nelson Espeland and Michael Sauder, Rankings and Reactivity: How Public Measures Recreate Social Worlds (2007).
  • Wendy Nelson Espeland and Mitchell L. Stevens, A Sociology of Quantification (2008).
  • Andrea Mennicken and Wendy Nelson Espeland, What's New with Numbers? Sociological Approaches to the Study of Quantification (2019).
  • Marion Fourcade and Kieran Healy, Seeing like a market (2016).
  • Barbara Kiviat, The Moral Limits of Predictive Practices: The Case of Credit-Based Insurance Scores (2019).
  • Katherine C. Kellogg, Melissa A. Valentine and Angèle Christin, Algorithms at Work: The New Contested Terrain of Control (2020).

Every sentence in a quotation block is ASI 2030's record, word for word. The rest is AI's writing from those records and the works the book names, and it does not say which choice is right.

RISCON Labsrisconlabs.com