Bibliographic record
Abstract
Is there no future in predictions?N'y a-t-il pas d'avenir dans les prédictions?O I' , "W the ideas for your editorials?Don't you ever run out of things to rant about?"I'm happy to respond that, to date, I haven't experienced any major episodes of writer's block and have, in fact, an almost too wide variety of sources providing inspiration.is month, it's a work of non-ction by Canadian author and columnist, Dan Gardner -Future babble: Why expert predictions fail -and why we believe them anyway. 1 In a nutshell, the book looks at the psychology of why humans have an endless appetite for predictions and then willingly ignore the facts when the predictions don't pan out.Reading this book, of course, caused me to re ect on some of my own writing, particularly a column where I thought I had a future in fortune-telling.Since Gardner quotes research stating that the average expert prediction has no more chance of being right than a ipped coin, 1 I thought it was time to revisit that article from July 2010 2 and see how well my predictions have stood the test of time.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".