How Does the Short-Term Training Program Contribute to Skills Development in Bangladesh? : A Tracer Study of the Short-Term Training Graduates
Why this work is in the frame
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Bibliographic record
Abstract
Skills development is one of the \n priorities for national economic development strategies of \n Bangladesh. The vision 2021 of the Government of Bangladesh \n gives the highest priority to building a large base of \n skilled workers in order to achieve a poverty-free \n middle-income country by 2021. The skills development sector \n is highly complex due to multiple service providers, a vast \n spectrum of target audiences, a large range in modalities of \n service provision, and varied emphases in terms of skills \n levels and types. The short-term training, a formal channel \n of six months training, is an important instrument for \n bridging the gap between the needs of the labor market for \n increasing the pool of skillful workers and the aspiration \n of the students for finding a good job. In order to assess \n the performance of short-term training and interventions by \n Skills and Training Enhancement Project (STEP), a tracer \n study was conducted between December 2013 and January 2014. \n Skills and Training Enhancement Project (STEP) is jointly \n financed by the World Bank, Canada and the Government of \n Bangladesh (GoB), which started in 2010 for contributing to \n Bangladesh’s medium to long-term objective of developing its \n human resources as a cornerstone of its strategy for poverty \n alleviation and economic growth. It supports competitively \n selected 42 public and 8 private short-term training \n institutions for improving the quality of training and \n providing opportunities to the disadvantaged youth for \n obtaining skills from the select training providers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it