The shadow pandemic and the divine feminine in the diaspora: An analysis of Deepa Mehta’s Heaven on Earth
Bibliographic record
Abstract
This article engaged in a literary analysis of Deepa Mehta’s Heaven on Earth , with a specific focus on the shadow pandemic being domestic violence in the Indian diaspora, and on the film’s representation of the divine feminine in Indian culture. By using the lens of Hindu mythology, the feminine divine was given prominence. The film centres on the Indian diaspora in Canada. The Canadian diaspora was similar to the South African diaspora through its depiction of Indian and African people living together and experiencing a shared knowledge with specific reference to traditional medicine. Through Heaven on Earth , Mehta offered an alternative to hegemonic patriarchal religious depictions and a varied perspective on gender by highlighting the essential role of the divine feminine. The term ‘shadow pandemic’ denoted domestic violence as a ‘pandemic’ that has scourged across the world, exacerbated by the advent of the COVID-19 pandemic. Through an analysis of culture and female divinity, the main female protagonist of the text was able to exit an abusive relationship and enter into her own female power. This feminine agency was an important resource for countless women trapped in abusive relationships. Contribution: The discussion in this article centres on a literary analysis of Deepa Mehta’s Heaven on Earth ( 2008 ) with emphasis on domestic violence and the shadow pandemic with specific emphasis on women of colour in the diaspora. The analysis also makes use of a cultural lens to discuss both the snake and androgyny in diasporic Indian culture providing a counter-stance to patriarchy. This research can be utilised by hermeneutists of suspicion and specialists in the field of public theology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".