Deciphering economic futures: Electricity, calculation, and the power economy, 1880–1930
Bibliographic record
Abstract
More than other energy industries, the electric power industry relied on calculating practices and codifications like load management to handle and develop their technical systems. Scholars have approached these practices largely from the point of view of the history of electricity. While it is true that these practices facilitated the expansion of the industry, this paper argues that electrical systems and the calculations around them were also used to “decipher” the new relations between power, economic change, and society that were emerging in the first decades of the 20th century. This paper asks how electricity took on the form of a mode of representation of economic life. Starting from the control of currents in early electrical systems via the calculation of voltage, current, and resistance, the paper shows how load management developed, and how the calculations around the large, interconnected power systems of the early 20th century were used as information on the “power economy.” In the medium of the power economy, engineers, economists, and politicians imagined the relation between the national and the world economy, between technical progress and the nascent macroeconomic object of “the economy.” Based on an analysis of the contributions to the World Power Conferences, the paper distinguishes two ways in which calculations around electricity became relevant for economic policy in the interwar years: as an indicator of economic growth and as the ground for a new economy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".