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
Technological innovation has historically contributed to inclusive economic growth in Germany. In more recent decades, however, this contribution has weakened due to the declining impact of technological innovation on labor productivity growth. Fearing that this declining impact would undermine the international competitiveness of the economy, real labor compensation was progressively curbed since the mid-1990s. This occurred inter alia through the government's erosion of the social welfare state, as well as through offshoring and reduced fixed capital investment of the corporate sector. The outcome was rising income and wealth inequalities. Between the mid-1990s and 2010 the rise in wage inequality was faster in Germany than in the United States, the United Kingdom, and Canada. To restore inclusive growth, two broad policy measures are recommended: first, to have appropriate compensatory social welfare policies in place; and second, to improve the effectiveness of technological innovation to raise labor productivity. This paper identifies three reasons why technological innovation has become less and less effective:(i) historical legacies, (ii) weaknesses in the education system, and (iii) entrepreneurial stagnation. Improving the impact of technological innovations on labor productivity growth will require a more diversified education system, a deepening of active labor market policies, better immigration policies, and a greater contestability of markets. Ensuring these recommendations in a coordinated fashion suggests the need for an appropriate industrial-innovation policy.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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