Bibliographic record
Abstract
Abstract The Uppsala internationalization process (IP) model is one of the few long-standing developments in the international business (IB) field to have maintained pre-eminence into the early 21st century. Scholars at Uppsala University were early proponents of research on firm internationalization processes, advancing a model that defined the “Uppsala School” in IB. The attention that the model has received is justified, attesting to its continued relevance since it was first proposed in the 1970s. While welcomed, this attention has, however, not succeeded in capturing the theoretical essence of this model and its potential in further developing IP theory. The consequence of this failure in the IB field is profound, as the full promise of research on the internationalizing firm, as well as the firm’s expansion into markets overseas, is yet to be realized. It can be asked, What then is the Uppsala IP model? Answering this question requires addressing common misconceptions about the model that have circulated for decades. These misconceptions continue to circulate, constraining theorizing on this topic. Dispelling these misconceptions and specifying what the IP model is provides the IB field fresh insights into what it could be when developed further in the future.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".