Self-promotion and the need to be first in science
Bibliographic record
Abstract
Scientists, like all humans, are subject to self-deceptive valuations of their importance and profile. Vainglorious practice is annoying but mostly harmless when restricted to an individual’s perception of self-worth. Language that can be associated with self-promotion and aggrandizement is destructive when incorporated into scientific writing. So too is any practice that oversells the novelty of research or fails to provide sufficient scholarship on the uniqueness of results. We evaluated whether such tendencies have been increasing over time by assessing the frequencies of articles claiming to be “the first”, and those that placed the requirement for scholarship on readers by using phrases such as “to the best of our knowledge”. Our survey of titles and abstracts of 176 journals in ecology and environmental biology revealed that the frequencies of both practices increased linearly over the past half century. We thus warn readers, journal editors, and granting agencies to use caution when assessing the claimed novelty of research contributions. A system-wide reform toward more cooperative science that values humility, and abhors hubris, might help to rectify the problem.
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 | 0.062 | 0.234 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".