Publication bias and merit in ecology
Bibliographic record
Abstract
Bias, or any set of factors that influence the general expression of merit, is common in science and is an inevitable by‐product of an imperfect but otherwise reasonably objective human pursuit to understand the world we inhabit. In this paper, we explore the conceptual significance of a relatively tractable form of bias, namely publication and dissemination bias. A specific definition is developed, a working model of classification for publication bias is proposed, and an assessment of what we can measure is described. Finally, we offer expectations for ecologists with respect to the significance of bias in the publication process within our discipline. We argue that without explicit consideration of both the qualitative and quantitative aspects of publication bias in ecology, we limit our capacity to fairly assess and best use the science that we as a community produce.
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 | 0.221 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".