Do Female Enrolment Rates Cause Economic Growth in Pakistan?
Bibliographic record
Abstract
There has been much discussion on the relationship between education and economic growth. A few studies have examined the increasing trend of female enrolment in educational institutions and economic growth. The objective of this paper is to empirically investigate four alternatives but equally plausible hypotheses. These are: i) GDP cause female enrolment proxies (the conventional view), ii) Female enrolment proxies cause GDP, iii) There is a bi-directional causality between the two variables and iv) Both variables are causality independent. In order to find the relationship between the two variables set, a time series Co-integration and Granger Causality Tests have been employed separately. Secondary data pertaining to Pakistan from 1966 - 2008 has been used for analysis. The empirical results moderately support the conventional view that GDP has significant long-run casual effect on the female enrolment proxies in Pakistan. The present study supports the unidirectional causality relationship between the GDP and female enrolment in the specific context of Pakistan.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".