Bibliographic record
Abstract
ion is at the heart of mathematics. The power of mathematics in society rests on its claims to truths that transcend human subjectivity and culture. For example, for commerce to be fair it is very important that all parties agree on how to find the sum of a set of numbers. The result should not depend on how rich or poor you are or on your ethnic background. In such instances a frozen and static mathematics is a powerful tool for making convincing arguments. This is especially important in democracies, in which argument is supposed to be based on dialogue, not status. However, even when people use mathematics in deliberation (or argument), they have to decide to agree that mathematics is an appropriate tool for the problem being addressed. Furthermore, when we choose mathematics, we have to choose what to count and how to use the results of our counting. Though mathematics is cold and static, it has to be picked up and manipulated as a tool by a warm human body who makes decisions about how to apply the tool. Mathematics can be a dangerous tool for exploitation if some people know they can make choices with it and others believe that mathematics is free from human choices. Those who believe that mathematics is values-free or independent of culture are open to being manipulated by others who are savvy with their mathematics.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.062 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".