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
I analyse TFP growth at the sectoral and aggregate level, using data for 10 industry groups covering the market sector for 18 countries over the period 1970-2007 drawn from the EU KLEMS dataset. TFP growth displays persistence at the aggregate level but not at the industry level, suggesting industry outputs are measured with error. In all countries resources have been shifting away from industries with high TFP growth towards industries with low TFP growth. Nevertheless I find that structural change (as measured by changes in value added shares) has favoured growth in most countries. Errors in measuring capital or in measuring the elasticity of output with respect to capital are unlikely to substantially reduce the role of TFP in explaining growth. The pattern of growth in these 18 countries is more consistent with an underlying two-sector model than with the one-sector (Solow) model. Standard theory suggests that TFP growth induces capital accumulation, at least in the long run. This is not the case with the raw EU KLEMS data used here. But standard theory finds some support when the data are smoothed to remove cyclical effects.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 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