Sixty years of ecology with impact
Bibliographic record
Abstract
Abstract Journal of Applied Ecology celebrates its 60th birthday in 2024. In this Editorial, we explore how the journal's role has changed since its launch and investigate whether the articles we publish are achieving real‐world impact. We designed and ran an author survey for all authors who have published with us between 2017 and 2021. Authors were asked if their publication achieved real‐world impact, and if so, how they achieved it. Forty four percent of respondents achieved real‐world impact with their article, primarily citing engagement with key stakeholders as the reason for this impact. We also assessed our impact on online policy documentation, comparing this to our citations in the published scientific literature. We are the most highly cited British Ecological Society journal for policy mentions with over 2800 citations in total. We also found a weak correlation between policy citations and citations in academic literature, which highlights the fact that article with relatively few academic citations can have large real‐world impact. Synthesis and applications. Whilst these results are encouraging, there are significant challenges involved in achieving and measuring impact scale. To help address some of these, we launch here a suite of new author services to help our authors achieve real‐world impact with their work. This includes offering plain language summaries and the opportunity to present findings to British Ecological Society's stakeholder community.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".