Economics of technology cycle time (TCT) and catch-up by latecomers: Micro-, meso-, and macro-analyses and implications
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 paper provides an analytical review of the literature on the role of technology cycle time (TCT) in the catching-up process of latecomers at the firm, sectoral, and national levels. At the national level, latecomer economies follow a detour that consists of economic growth through specialization in short-TCT sectors during the catching-up phase, followed by a shift to long-TCT sectors in the post-catching-up phase. The paper then discusses the double-edged nature of TCT at the sectoral level, such that short TCT can either be a window of opportunity associated with the rapid obsolescence of existing technologies and thus low entry barriers, or another source of difficulty associated with the truncation of learning from existing technologies. Only latecomers with a certain absorptive capacity can benefit from short TCT as a window of opportunity. Finally, at the firm level, this paper discusses the issue of possible convergence in the behavior of catching-up firms towards those of mature firms in advanced economies. At all three levels, the keywords are detours and convergence. Given the barriers to entry in long-TCT sectors, latecomers pursue a strategy of detouring into short-TCT sectors. That is, instead of trying to emulate incumbents by entering long-TCT sectors, latecomers take the opposite route. Subsequently, as latecomers improve their capabilities over time, they shift their specialization from short to long TCT sectors, thereby achieving convergence in behavior and strategy at the firm, sectoral, and national levels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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