On the Mechanics of Measuring the Production of Financial Institutions
Bibliographic record
Abstract
Abstract * With the emergence of new technologies, innovative financial instruments and the integration of global market operations national accountants are confronted with a series of new challenges and complexities in determining the output of financial institutions. The SNA93 recommended measure, the FISIM – financial intermediation services indirectly measured- which focused around the traditional deposits and loans business is currently under scrutiny as numerous views have been expressed questioning its validity in measuring the financial output. Critics have argued that the FISIM, as is defined, fails to capture the recent developments in financial markets and the technological advances and thus undermine the true economic contribution of these institutions. Several OECD task groups have examined this issue and currently considering changes to the existing methodologies. The goal of this study is two-fold. First, it explores a number of emerging areas in finance that are critically important for national accounting purposes. Second, by using the latest I-O tables the paper examines empirically how the recent technological advances and the global market environment have impacted the Canadian financial sector and provides some insights as to how the current SNA practices could be extended to account for such changes.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".