Cammini LTER: walking and cycling with citizens across Italian ecosystems
Bibliographic record
Abstract
Italian ecologists, active in Long-Term Ecological Research (LTER-Italy network) and in biodiversity study (LifeWatch-Italy), launched a process of informal science communication, aimed at increasing ecological awareness and literacy, also through Citizen Science activities. Since 2015, they organized “Cammini LTER”, trails connecting a number of LTER-Italy sites, where researchers walked and cycled with citizens, sharing research questions, methodologies and results. The citizens’ engagement through a physical and visible movement of researchers outside their laboratories enhanced the opportunity to familiarize with a wide variety of Italian ecosystems and with the LTER data acquisition procedures. The slow rhythm of walking or cycling offers the possibility to overcome an exclusively rational and cognitive approach, breaking down the barriers between science and society, and bringing emotions and affectivity into the ecological subjects. Cammini LTER generated a process of mutual learning with the public and induced critical considerations among researchers about relevant aspects of science literacy and public engagement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".