A Biodiversity Literacy: A Systematic Literature Review of Conceptual Frameworks, Educational Strategies, and Policy Implications (2015-2025)
Bibliographic record
Abstract
As reported in our data, global biodiversity is in crisis due to ecosystem changes caused by human activities over thousands of years.This crisis threatens the achievement of sustainable development, making biodiversity literacy, which encompasses essential skills in conservation, a key component of sustainable programs.This article presents a systematic literature review (SLR) aimed at identifying aspects and concepts focused on approaches in educational practice and policy implications to support biodiversity literacy, thereby limiting the selection to articles focused on efforts to promote biodiversity literacy.The search was conducted in the Scopus database to find relevant articles published between 2015 and 2025.Articles were included and excluded based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting standards, followed by an independent double-anonymized analysis process.From the search results, 302 articles were identified, 32 of which met the analysis criteria.The review findings indicate that biodiversity literacy encompasses knowledge, attitude, awareness, and biodiversity action.The findings also reveal variations in the conceptualization of biodiversity literacy among different 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 | 0.032 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.042 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".