Bibliographic record
Abstract
The educational external efficiency is viewed as the accountability of the educational system in various personal, social, economic and political aspects, which by understanding factors affecting the development of its status, it is clear that the reason for the non-fulfillment of the functions of education in a society is affected by qualitative and quantitative issues of education or lack of educational equity and institutional quality.Understanding the factors allows a country to be able to control most functions of education achieved without more abundant economic resources allocated to the educational system.This paper measures the efficiency of production process of education in Iran during 1972-2013 using a semiparametric DEA input-based with constant returns to scale, and is able to identify surplus and shortage of inputs and outputs on inefficient units.Then, there are three steps to identify determinants of efficiency status.The first is a sensitivity analysis of efficiency in order to prioritize the inputs in efficiency, secondly, the separation process of the formation of the efficiency of external education into two sub-processes, in which first process efficiency (turning education into knowledge) is under internal efficiency of education and second process ( turning knowledge into understanding) under the title of knowledge efficiency and thirdly, using time-series regression methods to examine the effects of "environmental factors" on the efficiency of external education.The results show that the external efficiency of education is achieved in 87% of the years studied.The high-output deficiency in order of importance in the external efficiency is related to production, environmental sustainability, productivity, stability and security of the family and the highest surplus is in expenditure on education and per capita teacher for per student.The major source of inefficiency is related to the second process, i.e, the process of transforming knowledge into understanding.Environmental factors of internal efficiency of education, knowledge efficiency, employment rate and economic independence, have a significant positive effect on the external efficiency of education and black economy (underground) volume has a significant negative effect on the external efficiency of environmental factors.Among studied environmental factors, the employment has the greatest impact on external efficiency of an education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.942 | 0.924 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".