Bibliographic record
Abstract
This study aims to examine how the lives of blacks are reduced and eliminated in Brother (2017) by David Chariandy. Black Lives Matter is a hash tag that appears after the killing of Trayvon Martin (17 years old African American) in 2012 by the savage hands of George Zimmerman (white person). This hash-tag has become a social movement that calls for equality in order to stop the violence against black people because their live is as valuable as white’s. The movement comes into being to highlight the “hypocritical democracy in service to the white males whose freedom are openly depended upon the oppression of blacks” (Lebron, 2017, P. 1). Those who have started this movement try to redeem a state and its arbitrary actions against black who are exterminated since the slavery. Alicia Graza, Patrisse Cullors, and Opal Tometi have established this movement to reveal the suffering of the blacks who have no rights to live their life. Chariandy is a Canadian writer who specialized in Caribbean literature, black diaspora, and postcolonial studies. The novel is analyzed through Kimberlé Crenshaw’s concept (intersectionality) to show how the race, gender, and class are intersecting together to emphasize how the human beings will be treated accordingly.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| 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".