History and evolutions, trends and future perspectives in Environmental Psychology
Bibliographic record
Abstract
pThe organizing committee of the 23rd IAPS International Conference “Transitions to sustainable societies: Designing research and policy for changing lifestyles and communities”, which will take place in Timisoara, Romania, 23 – 27 June 2014, is organizing the symposium “History and evolutions, trends and future perspectives in Environmental Psychology”./p\npIn organizing this symposium, we aim to bring together leading experts from Environmental Psychology from different parts of the word to analyze and discuss with the research community, the history, evolutions and future perspectives of our discipline./p\npWe believe that some important issues should be addressed/p\nullithe History of Environmental Psychology in different parts of the world (Europe, USA, Canada, Latin-America countries, India, Japan, etc.)/li\nlithe evolution on main domains in EP, like as: Psychology of Urban Environments, Housing and Quality of Life, Public perception in Environmental Policy, Environmental Risk and Vulnerability/li\nlithe evolution of Sustainability related with psychological concepts and constructions ./li\nlicurrent trends and future directions of EP/li\n/ulpWe will invite professor Christopher Spencer, professor David Uzzell, professor Roderick Lawrence, professor Eric Pol, professor Robert Gifford, but also other colleagues who shown during the time interest to this approach./p
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".