Bibliographic record
Abstract
The prefix “pseudo” is derived from a Greek word meaning “false.” Pseudoscience refers to theories, assumptions, and methods that are mistakenly thought to be scientific. Obviously the distinction between science and pseudoscience is not clear-cut. Pseudoscience overlaps with scientific “misconceptions” - beliefs that are held by students and the general public that differ from scientific fact. Both of these concepts are subtle ones, since all scientific theories can be regarded as tentative to some degree. At the 1996 IAU conference on astronomy education in London, UK, Neil Comins classified astronomical misconceptions into about 20 types, and he has described these in detail in his book Heavenly Errors (Columbia University Press, 2001). Some misconceptions are cognitive in nature; they are dealt with in our Part II. Others are products of religious belief or superstition (often transmitted through the “authority” of family or friends), or popular culture, or errors or excesses of the media. In relation to astronomy, notable pseudosciences include astrology, space aliens, and creationism; the works of Velikovsky also fall under the pseudoscience rubric. The majority of students will be affected in some way by these beliefs. How to deal with them? The theory of constructivism is one of the most influential sciencelearning theories in schools today. It states that, by reflecting on their own knowledge and experiences, students build their own new knowledge about the universe around them. Teachers must therefore be aware of students' pseudoscientific beliefs, as well as their other misconceptions, if they are to correct them through their teaching. Many of them are deeply rooted. They cannot easily be changed by lectures and textbooks. They must be confronted through minds-on teaching.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.405 | 0.233 |
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".