Bibliographic record
Abstract
This article takes an unconventional approach to the issue of ‘race’. It illustrates the complexity and inter-disciplinarity of the racial phenomenon by blending child-like and adult-like perspectives and, as such, it is written half as a fable and half as an academic article. It puts forward a number of arguments. First, the piece suggests that five key dimensions of racism exist—namely, the historical, the philosophical, the scientific, the legal and the economic—and that they are regularly used to justify and rationalise something (racial prejudice) that is often irrational. Second, the article highlights the ‘banality’ of racism—an attitude that stems from mankind’s diffidence towards diversity, exploits such fear and encourages forms of passivity that, as Hannah Arendt noted, are the best way of ‘banalising’ evil and making it appear tolerable. The fanciful classifications of ‘races’—such as those articulated in South Africa’s apartheid, where poor Chinese were regarded as ‘yellows’ while wealthy Japanese were classified as ‘honorary whites’—are cases in point. Third, the piece argues that racial discrimination is often unrecognised, internalised and unquestioned by society: since racial attitudes are learnt in childhood and become part of our cultural baggage, they are extremely difficult to identify and eradicate. Finally, the article suggests that racism is not only ‘banal’ but also highly convenient: racists do not need to know the individual, they just need to know the characteristics of the group to which that individual is thought to belong. The piece concludes in favour of a neglected right for children: the right to be left free from racism.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".