Racist disinformation on the World Wide Web: initial implications for the LIS community
Bibliographic record
Abstract
This paper has emerged from an Australian-based doctoral research program investigating the presence of racist disinformation on the World Wide Web (WWW) and the extent to which, if any, such material can be balanced by the content of anti-racist sites. Prior research about racism on the internet has rarely dealt specifically with the World Wide Web. Much of what has been written has focused on pornography in the censorship/free speech debate, with racism treated as an adjunct. Whereas previous researchers have raised the potential of the internet as a source of disinformation, there has been little in the way of specific studies of racist disinformation on the World Wide Web. This paper addresses a number of issues emerging from the relevant literatures and clarifies important points of terminology. Finally it considers possible implications for the role of the LIS community as use of the World Wide Web by racist groups increases (Institute of Race Relations 1999).
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".