Bibliographic record
Abstract
The purpose of the sudy was to ascertain the extent to which Staff Monitored Program (SMP), initiated by the Government of Angola, has affected the performance of firms based in Angola. This study focused on Angola because of several reasons: first, it appears that there is no academic study that examined the proposed relationship in Angola; second, the vast majority of studies have concentrated on Common Independent States (CIS), China, and India, but a few in the African Continent; and finally, Angola is one of the newest countries embracing the concept of economic reform. Certainly, the study provides insight into the role of free-market policies on firms' market performance in the transition economy. Based on the data obtained from 180 firms representing domestic and foreign businesses with most of their headquarters in Portugal, Brazil, Spain, Sao Tome, the USA and the UK, multiple regression analysis indicated that foreign exchange reform policy and international trade reform policy were the major determinants of firms' performance. Further, the study found little support for the hypotheses that investment, and infrastructure development reform policies have led to improved firm performance. Finally, managerial implications of the findings and the limitations of the study are discussed along with future research directions.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".