Bibliographic record
Abstract
For those who are unfamiliar with the term, ‘mansplaining’ refers to a moment when a man takes it upon himself to educate others with a "delightful mixture of privilege and ignorance that leads to condescending, inaccurate explanations, delivered with the rock-solid conviction of rightness and that slimy certainty that of course he is right, because he is the man in [the] conversation." Since its introduction to the English language in the early 21st century, mansplaining has generally been used in reference to women’s rights and feminism’s struggle with cultural misogyny. However, the term serves as an excellent metaphor to describe the situation that occurred in the focus of this essay – a radio symposium about science and religion. The men who spoke in the symposium were regarded as experts in their fields mostly due to their privilege, class, race, and gender – none of which they earned and none of which qualified them to talk about the subject. Rather than men explaining women – as is the case with mansplaining – this symposium shows white Christians explaining non-white non-Christians. This ‘white-splaining’ or ‘Christian-splaining’ resulted in a prejudiced, ignorant worldview which was spread throughout the Western world. This symposium is one of many similar bricks that built the foundation of racist and oversimplified conceptions of world religion in the generalized Western consciousness today.
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 | 0.009 | 0.021 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".