Is the fourth industrial revolution a continuation of the third industrial revolution or something new under the sun? Analyzing technological regimes using US patent data
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
Abstract This study has compared the technological regimes of the Fourth Industrial Revolution (4IR) and the Third Industrial Revolution (3IR) technologies. If we limit the comparison based on absolute values representing diverse elements of the technological regime, 4IR technologies are more original and science-based and have a longer technological cycle time (TCT). However, all these differences turn insignificant or reversed when the comparison is made using normalized values of variables reflecting over-time trends. Moreover, 4IR technologies, including artificial intelligence (AI), have an impact on subsequent innovations in less wide fields (lower degree of generality) compared with 3IR, which means that they may not be counted as a new general-purpose technology. Furthermore, a longer TCT of 4IR technologies, including AI, means that they tend to keep citing 3IR technologies or older. In this sense, 4IR technologies are not much a radical break from past technologies but tend to be evolutionary, whereas 3IR technologies correspond to a more radical break from the past technologies because they have a shorter TCT and rely less on old technologies. At the aggregate level, technologies in the 21st century heavily rely on science, combining knowledge from more diverse fields (higher originality) and becoming longer cycled but having impact on less diverse fields (lower generality), which is true not just in a few technologies commonly associated with the so-called 4IR but across the board of technologies. Finally, although five representative 4IR technologies do not command radically different technological regimes compared with 3IR technologies, they are still outstanding, that is, they have higher originality, generality, and shorter TCT compared with the average technologies in the 2010s.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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