Bibliographic record
Abstract
Abstract Mind the Science is the self-defense shield that we direly need to protect us against the onslaught of bogus mental health treatments and products that have increasingly flooded social media, popular media, and the business of health care itself. It provides a takedown of mental health misinformation and pseudoscience to educate and embolden those who wish to make informed decisions about their mental health. The first section equips readers with the necessary background. They’ll learn about the nature, evolution, and seduction of pseudoscience, and ultimately how to become science and mental health literate. Readers will come to understand how mental health misinformation can be traced back through history right up to the present collision of the anti-psychiatry movement and the wellness industry. The second section teaches how to spot misinformation and propaganda. It shines a light on various pseudoscientific practices, shows the psychological reasons that leave us vulnerable to believing misinformation, and helps readers to develop a keen eye for the tactics and tropes that are used to push propaganda in the wellness and alternative medicine communities. The third section offers solutions, showing the concepts and science behind evidence-based ways to improve mental health, and teaches what to look for when seeking real professional help. In the end, readers will be better positioned to identify mental health misinformation, to steer clear of misguided and predatory practices, and to understand what mental health really means.
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 | 0.005 |
| 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 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".