Book Review-Science Fictions: Exposing Fraud, Bias, Negligence and Hype in Science
Bibliographic record
Abstract
A shocking and captivating book about a topic every researcher, educator and decision maker should know, it first shakes you to the core, then explains all that is objectively known to be wrong with how we currently conduct scientific research, finally suggesting some possible solutions.Many of the problems identified will be known to a seasoned researcher: publication bias, p-hacking, and hyping correlation from observational studies that does not equal causation, while many researchers are also working collectively to solve these issues (e.g., Open Science Framework, n.d.).Nevertheless, having virtually all that is wrong with science summarized in one book is sure to move, inspire, and compel every scientist to take action.However, this book should not get into the hands of the public, for it will inject fear and despair, washing away the boundary between science and pseudoscience.The book has had an incredible reception.In 2021, it was short-listed for the Royal Society Prize for Science Books (Bookseller, 2021), although it lost to another excellent book.The author, Stuart James Ritchie, is a lecturer at King's College London, currently with 8022 citations and an h-index of 43 (Ritchie, n.d.).As a researcher in psychology, he experienced early in his career that many journals (used to) refuse replication studies, the core of what makes science, science, embarking on a quest to change it (Ritchie, 2021).The book supports its arguments exceptionally well.Every chapter consists of several flawed research papers, followed by other researcher papers rebutting those papers, and sometimes followed by papers refuting the rebuttal!Incredible.A quarter of the whole book consists of explanatory notes and references to literature, which is placed at the end, so as not to disturb the flow of reading.At one point, Ritchie criticizes a Nobel prize winner, Daniel Kahneman, for relying too much in his best-selling book (2012), on some work that was later discredited.Yet, the same could go for this book, too.In the author's own words: "... even if you read a seemingly devastating critique of a piece of research, the critique itself might be mistaken, and so might be the critiques of the critique.That also goes for everything I've written in this book."He offers a 5-pound reward for every minor mistake found in the book, and a 50-pound reward for a major mistake; this book shows that science is full of mistakes.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.017 |
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".