Toward rigorous use of expert knowledge in ecological research
Bibliographic record
Abstract
Practicing ecologists who excel at their work (“experts”) hold a wealth of knowledge. This knowledge offers a wide range of opportunities for application in ecological research and natural resource decision‐making. While experts are often consulted ad‐hoc, their contributions are not widely acknowledged. These informal applications of expert knowledge lead to concerns about a lack of transparency and repeatability, causing distrust of this knowledge source in the scientific community. Here, we address these concerns with an exploration of the diversity of expert knowledge and of rigorous methods in its use. The effective use of expert knowledge hinges on an awareness of the spectrum of experts and their expertise, which varies by breadth of perspective and critical assessment. Also, experts express their knowledge in different forms depending on the degree of contextualization with other information. Careful matching of experts to application is therefore essential and has to go beyond a simple fitting of the expert to the knowledge domain. The standards for the collection and use of expert knowledge should be as rigorous as for empirical data. This involves knowing when it is appropriate to use expert knowledge and how to identify and select suitable experts. Further, it requires a careful plan for the collection, analysis and validation of the knowledge. The knowledge held by expert practitioners is too valuable to be ignored. But only when thorough methods are applied, can the application of expert knowledge be as valid as the use of empirical data. The responsibility for the effective and rigorous use of expert knowledge lies with the researchers.
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 | 0.757 | 0.743 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.011 | 0.071 |
| Scholarly communication | 0.034 | 0.040 |
| Open science | 0.018 | 0.043 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".