Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The paper's purpose is to show that the reported (and growing) labour productivity gap between the G7 and OECD countries and the USA might be a factor of the rapid adoption of shadow banking structures and techniques in the USA versus the adoption of those structures in OECD and G7 economies. Design/methodology/approach The paper explains the concept and practice of shadow banking and explores the ways in which the various conventions adopted distort reported productivity figures. Findings The growing adoption of shadow banking over the period 1974‐2007 has had the effect of increasing the metrics for labour productivity over the same period. Practical implications It is clear that those who wish to understand the apparent growing gap between labour productivity of the USA and other G7/OECD nations must look beyond the simple reported figures to identify the ways in which figures are calculated and reported. Originality/value The paper shows that reporting of figures to established conventions can be affected by a range of factors, not apparent from looking at those conventions themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it