Bibliographic record
Abstract
Abstract Latecomer economies are firms which may be able to leapfrog older vintages of technology, and make pre-emptive investments in emerging technologies to catch up with advanced countries in new markets. Leapfrogging can be defined as latecomers trying something different ahead of the forerunners, thereby leaping over them. The answer to the question whether the fourth industrial revolution represents a new window of opportunity for leapfrogging or whether it constitutes a source of further risks for latecomers is that this depends entirely on the country’s response and readiness, that is, its industrial policy, digital literacy, the skill and education level, as well as domestic market size and position in the GVC. Policy recommendations for leapfrogging can also be made for different types of firms, such as incumbents and start-ups. The former comprises three types of firms, namely leaders, followers, and laggards. Path-creating type leapfrogging is more likely to take place in start-ups because they have invested the least in existing technologies or business models. Leader or follower type firms in emerging economies tend to have some experience with technology and absorptive capacity and are thus likely to be in a position to skip one or several stages, while managing the risks associated with leapfrogging. Lastly, laggard firms should not attempt pre-mature leapfrogging but should first build some absorptive capacity in their niche area and upgrade by moving up the higher end of the GVC.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".