MAGIC BUTTON OR DISASTER BUTTON AT THE BACK DROP OF SURVIVAL AND ASSIMULATION IN RICHARD MATHESON'S BUTTON BUTTON AND SHAUNA SINGH BALDWIN'S ONLY BUTTON
Bibliographic record
Abstract
Shauna Singh Baldwin, an internationally praised, award winning novelist is an Indo-Canadian diaspora writer who belongs to the Second Generation diaspora writers of SouthEast, mixed of three cultures: India, Canada and America, Baldwin writes from the perspective of these three cultures. Darwin's law of survival of the fittest continues to the modern age. The concept of globalization created a peculiar form of scattered lives which challenges the home land and foreign land. Displacement forces the process of assimilation and adaptability. It is an inevitable process. Richard Matheson is popularly known for his science fiction. Matheson’s short story “Button Button” is a fine example of his exceptional quality of work. The story consists of real ideas of one’s life; those who see it or read it get involved with characters. The present paper explores the symbol is m and narrative techniques in these two stories "Only a Button '' and "Button Button". Only a Button is a story from Shauna Singh Baldwin 'We are not in Pakistan' and the story Button, Button by Richard Matheson. “We are not in Pakistan” is a collection of ten stories inspired by human values and frailties. The characters in these stories are men and women, old, rich and poor, like able and hateful. They are from different nationalities and religions: Sikh, Jewish, Christian, Canadian, Pakistani and Ukrainian. Baldwin focuses on global events such as 9/11, Perestroika, glasnost, Chernobyl and its impact on middle class families.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".