Bibliographic record
Abstract
In the ‘Foreword’ to his collection Uhuru Street, Vassanji observes that ‘Uhuru’ means ‘independence’. The Kichwele Street of Dar es Salaam – later renamed as Uhuru street nurtures the spirit of independence irrespective of the continual changes that the street experienced from the sheltered innocence of colonial rule in the 1950s to the shattered world of the 1980s. This collection of short stories – as many of Vassanji’s works is characterized by “a complex ethno-cultural identity” that incorporates multiple countries (Kenya, Tanzania, India, Canada, U.S.A.), religions (crucially, the syncretic bhakti tradition he was raised in), languages (Gujarati, English, Swahili, Hindi). , The stories in Uhuru Street explore political and social change in the city of Dar es Salaam in the East African country of Tanganyika. They follow a historical arc which begins in the years leading up to independence (in 1961) and concludes in the decade or so. This paper analyzes the microcosm of an immigrant world as portrayed by Vassanji in his Uhuru Street through its eccentric characters giving us a portrait of a place and a people losing their innocence. The stories come together as a story of generations new and old, the former searching for a new identity, the latter, fiercely holding onto the past. We share with these people the moment of moving on, of leaving the place where we have roots, knowing that things will never be the same.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".