Bibliographic record
Abstract
Abstract Previous work has found that a small number of export s uperstars contribute disproportionally to the economy’s overall exports. Differently from export superstars , this study is the first to define high growth exporters (HGXs) (that are not export superstars ) as a new firm category. We provide their economic importance and depict their micro-level anatomy. By tracking HGXs in Croatia for over a quarter of a century, 44 out of 100 export superstars in 2019 were previously HGXs. HGXs represent only 0.5% of all firms and 18% of high growth firms (HGFs) in the economy, but are responsible for about 25% of new exports and 5% of new jobs. During their growth episode, HGXs hire more employees from technology intensive industries with previous experience in exporting. They often hire on a single year work contract, and more frequently send new employees to work abroad. HGX also increase their presence in more advanced markets, increase the number of new export products and decrease their reliance on the largest product or largest export market. We argue HGXs represent an under-researched category of firms.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".