M<scp>arket</scp> E<scp>xpansion and</scp> P<scp>roductivity</scp> G<scp>rowth</scp>: D<scp>o</scp> N<scp>ew</scp> D<scp>omestic</scp> M<scp>arkets</scp> M<scp>atter as</scp> M<scp>uch as</scp> N<scp>ew</scp> I<scp>nternational</scp> M<scp>arkets</scp>?
Bibliographic record
Abstract
This paper asks how market expansion contributes to productivity growth. It investigates whether entry to both new international markets and new domestic markets is associated with greater productivity growth. It also examines whether exit from export markets is necessarily associated with deteriorating performance or whether it too can lead to success if associated with movements to new markets. Finally, the paper drills down into the strategic stance of firms that move to new markets to examine the strategic differences that set them apart from their compatriots that do not find themselves able to adapt.
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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.019 | 0.023 |
| Meta-epidemiology (broad) | 0.022 | 0.012 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.022 | 0.011 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.000 | 0.021 |
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; both teacher heads agree on what is shown here.
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".