Challenges and Issues That Are Faced by the Sri Lankan School Library Staff (2000-2016 period)
Bibliographic record
Abstract
This study focuses on challenges and issues faced by the Sri Lankan School Library staff recruited under the General Education Project 2 (GEP 2). The study was conducted in order to identify the pros and cons of the GEP 2 Library staff training project. 4000 teacher librarians were recruited to the school libraries under the GEP 2 Project. The National Institute of Library and Information Science (NILIS), University of Colombo, was established under this project. In 2003 NILIS introduced Masters, Post-graduate Diplomas, Diploma and Certificate courses in Library Sciences, in order to train the newly recruited teacher librarians and support staff. This study proposes to reveal the main factors which contributed towards the inadequate number of students for the NILIS courses, which were mainly due to the policies of the government regarding staff training, and the school libraries in Sri Lanka. This study was carried out with the available written documents, communications, and face to face interviews, with the relevant parties. NILIS is struggling to improve the training of school library staff throughout the island in numerous ways, in spite of the red notice by the authorities to close down the Institute. Subsequently due to the best practice of NILIS it was possible to convince the officials of the Ministry of Education regarding the importance of School libraries, and staff training, which resulted in the increase of the number of students from 26 in 2013, to 250 in 2016.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".