“It is better to be wrong than boring”.
Bibliographic record
Abstract
It may seem bold, if not brazen, for the first words published in a new peer-reviewed journal to suggest wrong is right. It may seem scurrilous to suggest the broken clock is still right twice a day. And it may seem to most that only a dreamer would ponder the value of a simple stone found along a beach. Yet, commonly in the progress of science, it is necessary to be wrong before choosing the right path along the often circuitous journey that leads to truth. The broken clock owner needs to find a way to make the flawed timepiece right more often. Repair? Replace? That’s discovery. The stone on the beach may indeed turn out to be worthless, but another one discovered nearby becomes a beautiful sculpture when the inspired beachcomber is an Inuit artisan. That’s creativity. Wrong can be exciting, especially when it’s instrumental in the process of discovery. Creativity, challenge, and controversy are the catalysts of scientific enquiry. And so, acknowledging the complex and often nebulous interplay between wrong, right and boring, we launch Ideas in Ecology and Evolution
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".