Accompanying Data for “Research effort devoted to ecosystem services by environmental scientists and economists”
Bibliographic record
Abstract
Accompanying Data for “Research effort devoted to ecosystem services by environmental scientists and economists” Ecosystem Services Research Effort (ESRE) Kadykalo_etal_ESRE_data_1.csv = Extracted Data for “Research effort devoted to ecosystem services by environmental scientists and economists” Kadykalo_etal_ESRE_data_2.csv = Initial Economic Valuation Keyword Selection Kadykalo_etal_ESRE_data_3.csv = Keyword Sensitivity Analysis to Select All Keywords Kadykalo_etal_ESRE_data_4.csv = Final List of Keywords and Their Temporal Distribution (1995-2020) Kadykalo_etal_ESRE_data_5.csv = Saved Article Records for Searches of Economic Valuation and Environmental Sciences Literature on 15 Biophysical Ecosystem Services in Web of Science (Core Collection) and Scopus Kadykalo_etal_ESRE_data_6.csv = Cumulative Frequency of Article Hits in the Two Research Domains (Economic Valuation, Environmental Science) and Databases (Scopus, Web of Science – Core Collection) Kadykalo_etal_ESRE_data_7.csv = Research Effort Differential Between Environmental Science and Economic Valuation
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.287 | 0.130 |
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".