Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The unemployment environment in Pakistan is improving day by day because of the lack of interest of Govt. of Pakistan by not introducing investments friendly policies, rule of law and good governance; due to which foreign and local investors are not interests in making investments in Pakistan specially investors from Middle East multinationals playing an important role in uplifting the socioeconomic condition of the labor class, the most vulnerable segment of our society by providing pensioner benefits.The dominant role in this regard is of Government, it's to encourage the domestic investors for the domestic production of goods.For this purpose, the unemployment rate is regarded as dependent variable, and independent variables are population growth rate foreign direct investment and inflation rate.The unemployment rate in Pakistan showed increase since 2002 to 2015, which is strongly depended on population growth rate foreign direct investment.The results analyzed that the coefficient correlation of unemployment rate is negative affected from the changes in population growth.According to the above results, we can conclude that a one-unit increase in annual growth rate will decrease unemployment rate by 1.704 units.The foreign direct investment also shows negative trait with unemployment.According to the results, a oneunit increase in foreign direct investment will decrease unemployment rate by 0.002 units.Inflation has also negative relation with unemployment rate as through the results, we can see that a oneunit increase in inflation (CPI) will decrease unemployment rate by 0.25 units.The R square value shows that the model is good at explaining the variables.Because the value of R square is close to one, it means that dependent variables has a perfect correlation with independent variables.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.996 | 0.997 |
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 it