Bibliographic record
Abstract
D uring the last 30 years, the fi nancial services sector has grown enormously. This growth is apparent whether one measures the fi nancial sector by its share of GDP, by the quantity of fi nancial assets, by employment, or by average wages. At its peak in 2006, the fi nancial services sector contributed 8.3 percent to US GDP, compared to 4.9 percent in 1980 and 2.8 percent in 1950. The contribution to GDP is measured by the US Bureau of Economic Analysis (BEA) as value-added, which can be calculated either as fi nancial sector revenues minus nonwage inputs, or equivalently as profi ts plus compensation. Figure 1, following the methodology of Philippon (2012) and constructed from a variety of historical sources, shows that that the fi nancial sector share of GDP increased at a faster rate since 1980 (13 basis points of GDP per annum) than it did in the prior 30 years (7 basis points of GDP per annum).1 The growth of fi nancial services since 1980 accounted for more than a quarter of the growth of the services sector as a whole. Figure 1 shows 1 Online Appendix Table 1, which is available with this article at
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".