Nature conservation in a digitalized world: echo chambers and filter bubbles
Bibliographic record
Abstract
Digital echo chambers and filter bubbles are increasingly the subject of societal, political, and scientific discourse.However, the impact of these phenomena on nature conservation remains understudied.This study provides an explorative overview of the potential relevance of digital echo chambers and filter bubbles for nature conservation practice.For this purpose, data collected during a literature review as well as a digital expert survey of German conservation actors was evaluated.The data show that the phenomena are already considered in conjunction with conservation topics with a focus on climate protection in the scientific literature and nature conservation practice to a small but increasing extent.Furthermore, it is recognized that they pose more risks than potential benefits for nature conservation communication.However, the understanding of the exact processes associated with digital echo chambers and filter bubbles is insufficient.The study also identified an extensive need for action and research regarding the strategic consideration and handling of digital echo chambers and filter bubbles in nature conservation practice.There is significant potential to improve the societal acceptance upon which nature conservation depends and to increase the public participation in nature conservation issues.To make a responsible and effective contribution to society, nature conservation must keep abreast of new communication factors that are emerging in the age of digitalization.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".