Bibliographic record
Abstract
Every teacher, student and parent believes that reading is the most significant skill developed in elementary schools. To find the number of students who face difficulty in reading may be a point of debate, but one thing that everyone will be agreed to is the fact that a big percentage of students not only in India but also in the world are poor readers. Irrespective of the definition of a poor reader, there is a very high percentage of poor readers in our society. Incapability of reading is to a great extent, the most important reason that students are not growing academically according to their level or class, required to be provided special education or provided remedial classes. Among the students who are poor readers, there is a further gap between students from different geographical areas and genetic groups. These gaps and differences in reading skill have major consequences for our system and society, as they progressively lead to differences among Indian students that remain throughout their student life and even beyond that. The reading problem of Indian students is not new. Overall performance in reading has been more or less same since “National Policy of Education 1968”. The thinking that we can return to some earlier"golden age of reading" is wrong. The purpose of this article is to study the effectiveness of the campaign titled “Each child can read” being run by the Government of NCT of Delhi to improve the reading skill among students of the government schools of Delhi.
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.
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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".