Bibliographic record
Abstract
During 1988, thousands of Indo-Trinidadians ‘fled’ to Canada seeking ‘refugee status’, claiming that they were victims of racially inspired discrimination in their own country. Indeed, the collapse of the National Alliance for Reconstruction (NAR) coalition helped to fuel the embers of disenchantment which had smouldered in the breasts of many Indians who believed that the larger society saw them as pariahs and a group apart rather than as fully incorporated members of the national community (Ryan, 1999: 46). The contemporary view is that ’Race should not be used as a weapon to destroy citizens of this country’, so said Minister of Legal Affairs and leader of the Congress of the People (COP), Prakash Ramadhan yesterday. Ramadhan said the issue of race was very serious and will destroy the nation if not dealt with properly. Because of its seriousness the COP will be heading a nationwide discussion entitled ‘Race politics 50 years of Independence’. Dr Lincoln Douglas who will be in charge of the discussion on race said: We understand that historically we inherited a politics of race and a politics of ethnicity. We are making the effort to construct a different form of politics — one that is based on equity and justice and social justice and the common good. (Sue-Ann Wayow ‘Ramadhan: Don’t use race as a weapon’, Daily Express , Monday, 14 November 2011) These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".