Troubleshooting algorithms: A book review of Weapons of Math Destruction by Cathy O’Neil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fact: we no longer control our lives, algorithms do. Mortgage-backed securities, college rankings, online advertising, law enforcement, human resources, credit lending, insurance, social media, politics, and consumer marketing; algorithms live within each one of these – collecting, segmenting, defining, and planting each one of us into arbitrary, unassailable buckets. The algorithms and the data that feed this process is what data scientist and international bestselling author, Cathy O’Neil, calls Weapons of Math Destruction (WMDs). In her captivating and frankly, bone-chilling account of the power amassed by algorithms, O’Neil sheds much needed light into the seemingly omnipotent world of destructive algorithms. Keywords: algorithms, algorithmic transparency, algorithmic bias, communications, public relations, ethics, data, predictive models
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it