Unveiling New Feminist Insights into the Inspiring Narrative of Women Pioneering India’s Accomplished Mars Mission in the Film Mission Mangal
Bibliographic record
Abstract
The present research article delves into the transformative journey of women in society, with a particular focus on their education and emancipation, as depicted through the lens of cinema. In recent years, women have evolved from traditional roles to multifaceted individuals who actively shape their destinies. This transformation encompasses their education, as they gain access to knowledge and learning, and their emancipation, as they assert their rights and seek opportunities for personal and professional growth. Furthermore, this study explores the representation of these transformations in films, examining how cinema is a potent tool for educating and empowering women. “There are various works that deal with feminism through writings and movies which bring out the nature of women in a way that every woman can relate and reflect it with their daily lives.” (Shalini & Alamelu 2018) It investigates the portrayal of women’s lives and their journey towards self-realization and sheds light on the societal dynamics and challenges they encounter. Through cinematic narratives, women’s stories gain the potential to inspire and mobilize positive change, challenge existing norms and promote gender equality. The present study employs content analysis as the methodological approach, and this study seeks to provide a comprehensive analysis of the representation of women in “Mission Mangal” from a feminist viewpoint, revealing fresh insights into the portrayal of women’s roles and contributions in India’s Mars Mission exploration story.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".