Review of: "Assessing Sex Education Awareness Among Higher Secondary School Students in India"
Bibliographic record
Abstract
should comprise background, aim/objective, method, brief result and conclusion.2. Keywords should follow alphabetical order or according to MeSH system.3. The introduction of this article could benefit from additional references and context.For instance, incorporating information about the history of sex education could provide a broader perspective.Additionally, discussing societal issues related to sex education, such as misconceptions or controversies, could highlight the relevance and urgency of the topic.It's also important to address the low awareness of sex education among teenagers, as this is a key issue that the article seeks to address.By expanding the introduction in this way, the article could provide a more comprehensive and engaging overview of the subject.4. References should be put in order in the introduction or discussion.5. In the discussion section, it would be beneficial to compare and contrast the findings of this study with those of other relevant studies.This comparative analysis could provide a more comprehensive understanding of the topic and add depth to the discussion.By doing so, readers can see how this study fits into the larger body of research on the subject, and it could also highlight unique contributions or discrepancies that warrant further exploration.6.In the conclusion, it is recommended to make it brief.7. References should follow recommended citation style such as Vancouver or Harvard.Adjust it according to the journal's guidelines.These are the points from me.I recognize the urgency and importance of the issues addressed in this study.However, I believe that with some revisions, the paper could be significantly improved and its impact could be enhanced.
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".