Words and Numbers: Financialization and Accounting Standard Setting in the United Kingdom
Bibliographic record
Abstract
ABSTRACT How is it possible that British policymakers resisted market‐based measurement for decades while financial economic concepts of decision making and valuation still gained widespread acceptance as a justification for accounting standard setting? This study introduces the concept of “technologies of financialization” to develop the theorizing of the rise of finance in the domain of accounting. Based on a genealogical history of narrative reporting in the United Kingdom, it demonstrates how references to qualitative reporting techniques helped to address recurring crises of measurement from 1969 to 1993, and ultimately contributed to the practical acceptance of market‐based measurement in the UK standard‐setting context. The data are interpreted through a cultural economy framework that directs attention to the power of referring to financial reporting as a combination of words and numbers in sustaining its theoretical redefinition “from below”—that is, by relating it to the experience of practicing accountants rather than accounting theory. As a technology of financialization, narrative reporting made financial economic ideals of market‐based measurement, decision usefulness, and future orientation appear operable in a real‐life reporting context. Whenever measurement reached its practical limits, narratives were relied on to explain the impact of price‐level changes, frame economic decisions, and relate unobservable future cash flows to present‐day strategies and resources. The insight into how narrative reporting practices have been laced into the reasoning of capital markets for over 40 years is timely because it illustrates that narratives can also play a more encompassing role and drive the turn toward wider corporate accountability on social and environmental impacts while hard measurements in this area are still being figured out.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".