Perils of Heavy Rainfall: Displacement and Resettlement Driven by Floods
Bibliographic record
Abstract
Monsoon is typically a season to rejoice in South Asia because it cools off July's hot summer weather. In the poetry of Sufi mystic Shah Abdul Latif Bhitai, the monsoon represents a time of abundance, and his verses are prayers of abundance for Sindh and the entire world as rainfall is indeed a much-awaited season to cast off dry spells of the desert. However, in the past few years, climate change has led to heavy floods and massive displacement of poor people in Sindh. This year, floods even reached Karachi's urban city, the biggest metropolis of Pakistan, causing the displacement of 500,000 families and more than 1.2 million people. Amidst the outbreak of COVID-19, the displaced families face an even greater risk of being affected by the region's spreading virus in 2020. The soundscape composition, "Pitfalls of Heavy Rainfall," is based on field recordings collected from July to September 2020 in Karachi, my hometown. After teaching at Semester at Sea's Spring 2020 voyage that unexpectedly ended in South Africa, I, as a temporary resident, was denied entry to Canada and continued teaching at the University of Alberta's Faculty of Extension remotely. Going back to Pakistan after five years and living in Canada for more than 8-10 years as an international student, I have been experiencing displacement and reverse cultural shock alongside transformations due to COVID-19. I experienced a renewed appreciation of home and shelter after a growing sense of displacement due to COVID. This piece makes us vigilant of the inequities around us between children who can enjoy the rain and those who have to escape their homes to find shelter elsewhere. Rainfall does not make us all abundant. While some children have to save their goats and livestock and find another refuge because their houses have completely drowned, some children are still fortunate to be excited by the monsoon and singing to the skies and heavy wind.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".