Analyzing Canadian ecological restoration literature with bibliometric analysis and a systematic map. Presented at ESMARConf2021
Bibliographic record
Abstract
Our presentation will discuss a novel use of bibliometric analysis (using Bibliometrix) paired with a systematic map to characterize and synthesize a broad selection of research. Ecological Restoration Knowledge Synthesis (ERKS) is a nationally-funded knowledge synthesis project that has involved a systematic literature review, interviews and case studies to assess and synthesize the current state of ecological restoration knowledge in Canada. We will demonstrate how we used the Bibliometrix R package to draw conclusions about a selection of 3,013 peer-reviewed journal articles. The analysis from Bibliometrix highlighted key clusters of literature. We then conducted a systematic map on studies that measured the outcomes of ecological interventions. The bibliometric analysis process helped inform the scope of our systematic map by providing insights about the body of literature. The systematic map was conducted using CADIMA to track the exclusions and data extraction. The extracted data was analyzed using R to cluster the results and create heatmaps for specific subject areas. The two approaches taken together allowed us to synthesize the broad sweep of the academic literature.The resulting analysis blends bibliometric analysis with a systematic map, resulting in a methodology that can be used to characterize a wide body of subject-specific literature. Our talk will highlight how these two methods of synthesis work together to highlight gaps in the research landscape.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.174 | 0.259 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.317 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".