Hocus Pocus, Hexes and Healers: The Placebo Behind Magic
Bibliographic record
Abstract
My friend and I sat in the library late one afternoon frantically studying for finals. A nervous wreck, she could not help but panic at the thought of memorizing hundreds of molecules. At that very moment, I serendipitously received a message from my aunt who lived in Portugal—an expert spell caster who could ensure anyone a good grade on an exam. After telling this to my friend she quickly made a list of requests. My friend aced that final, but not because I informed my aunt of her pleas… Though witchcraft and the occult make very appealing explanations for the strange things that happen to us in our daily lives, these explanations do not stand the rigor of science. The so-called ‘magic’ that entices and enchants us, often stems from placebo effects and psychology. For better or for worse, the magic in our world has a significantly less supernatural explanation. Let us unveil the science behind the curtain.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".