What recent research does and doesn't tell us about rates of latecomer firms’ capability accumulation
Bibliographic record
Abstract
Summary Evidence of rates of capability accumulation in developing countries is crucial to further our understanding of timing of the process by which firms and industries move from production into innovative stages of technological progress. It is also a key input to support decision‐making on resources allocation for industrial development. Although this issue began to be systematically researched during the early 1970s, over the past 30 years the field has generated more hype around “industrial dynamics” than explicit analyses of how rapidly and why latecomer firms have moved into the accumulation of progressively innovative capabilities. However, there are a few exceptions. Drawing on a set of studies conducted under similar analytical frameworks from the 1990s, this paper reviews some of their merits and limitations in tackling speed of latecomer firms’ capability building. By exploring rates of firm‐level capability building in association with the organisational basis of the underlying learning processes, recent research has made relatively considerable advances that help broaden our understanding of this issue by showing the need for acceptance of variety in development paths, timings, and opportunities. Consequently, future research will need to couple surveys with intra‐sector/firm studies and longitudinal comparative analyses of capability building and learning to make meaningful interpretations of the kinetics of technological accumulation processes of firms and industries in current developing counties.
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 | 0.013 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".