Bibliographic record
Abstract
Place has a central focus in my work. All these stories take place in Islamabad. The city was built in the 1960s as the country’s capital and was home to families who worked in the government. This was an opportunity to write ordinariness, which is something Pakistan may not be particularly associated with. The capital works for these stories in different ways. Place is the backdrop (A Lizard on the Wall and Bankers) and place is the conflict for characters (Sher Khan in Crinkle Cut Yellow Fries, Alia in Lecturer and Tanvir in A Pair of Patent Leather Shoes). It is a refuge for characters like Alia, who is grateful to not be in rural Pakistan or in a more industrialized city, like Toronto. Tanvir faces violence and exploitation in the capital at the hands of law enforcement. Sher Khan has to learn guile and sell out to exist in the city. For Umair (Bankers), the city is a place of employment, but also of middle-class struggles to survive. As this develops into a collection, the absence of exotic narratives may characterize the completed work and, hopefully, will be a more realistic depiction of the Islamabad.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".