Bibliographic record
Abstract
This study sought to determine the effect of business regulation on social progress. The dependent variable, social progress, was measured in terms of social progress index of the sampled countries. On the other hand, the independent variable, business regulation, was measured in terms of business regulation score. Consequently, the study used secondary data from a sample of 248 countries over a period of five years (2014-2018). In order to determine the appropriate model for analysis, the study conducted the Hausman test where it was established that the random effect model was more appropriate as compared to the fixed effect model. Using the Stata computer program to run multiple regression analysis of the random effect model, the study findings indicated that business regulation has a positive and significant effect on social progress as given across all the six models that were estimated in this study. However, the overall effect of regulation, as given by the estimated regression coefficients under the respective models, kept varying with the introduction of an additional control variable. These findings were in accordance with the study expectations that business regulation significantly affects social progress. Further, the findings implied that, governments should devote additional resources towards addressing the social indicators of progress to meaningfully improve the living standards of residents, instead of solely focusing on economic and environmental factors. On the other hand, considering that the current study did not categorize countries according to their levels of development, it recommends for further research to determine the effect of business regulation on social progress in low-income, middle-income, and high-income countries to allow for comparison of findings from countries that are at different levels of development.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".