Measuring corporate diversity in financial services: a diversity index
Bibliographic record
Abstract
This paper provides a measure of corporate diversity in financial services. Our index is based on four components: ownership; competitiveness; balance sheet structure/resilience; and geographic spread. The first of these sub-indexes measures ownership diversity based on the Berry index of diversification and the Gini-Simpson index of biodiversity. It captures the extent of diversity in ownership types – for the UK, banks, mutuals, and the government owned National Savings & Investment – where each of these have different objectives, creating diversity in behaviour. Our second sub-index captures the extent of competition, and is based on the inverse of the Hirschmann-Herfindahl index of concentration. Our third sub-index measures diversity in balance sheet structures and resilience across the financial sector. Our final sub-index captures the extent of geographic spread and the regional concentration of financial services. These indicators are combined into a single index – the D-Index – that measures diversity in financial services. The D-Index shows a marked decline in the run-up to the 2007–2009 financial crisis, followed by further falls during 2008 and 2009. Since then, the index has remained more or less flat. We are no closer to creating the conditions – of diversity – to avoid a repeat of the 2007-2009 global financial crisis.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".