Travelling through ecosystems and biodiversity: Long-term ecological research for citizens
Bibliographic record
Abstract
Since 2015 Italian ecologists, active in long-term ecological research (LTER-Italy) and in biodiversity study (LifeWatch-Italy), started a process of informal public science communication, through walking and cycling together with citizens along itineraries connecting a number of LTER-Italy sites. The trails, named “CAMMINI LTER”, aimed at offering citizens an opportunity to familiarize with the components and conditions of Italian biodiversity and ecosystems, from the sea to alpine tundra. This initiative was conceived to share the research results among a large public, by creating a physical and visible movement of researchers towards and with citizens, relying as well on the slow rhythm of walking and cycling that allow to create an intimate link with people and nature. Cammini LTER intended, in particular, to promote LTER and LifeWatch activities to a not-expert audience and to increase ecological awareness and literacy, moving beyond communication deficit to dialogue, sharing scientific views as well as experiences and emotions. The trails were also an invaluable opportunity for scientists to understand the need to adopt a different cultural approach for more effective interactions with citizens, since communicating and sharing science is an important step for researchers to make their activity more visible and understandable. Actually, the trails produced unexpected effects on the scientists, evidencing the need of a cultural shift: they generated mutual learning by public and scientists and induced profound changes, vivid debates and critical considerations among researchers themselves, about some relevant aspects and needs of science communication. We report here some evaluations and perspectives coming form this experience that has recently crossed the national boundaries, becoming in 2016 the International initiative “TRAIL”, selected and launched by the International Long-Term Ecological Research Network (ILTER).
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads 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".