Citizenship Outcomes and Place-Based Learning Environments in an Integrated Environmental Studies Program
Bibliographic record
Abstract
This paper discusses the effects of the learning environment on an important and unique 21st century learning outcome—that of active citizenship, in contrast to more conventionally measured cognitive and attitudinal outcomes. In our study, we utilized a learning environment instrument, the Place-Based Learning and Constructivist Environment Survey (PLACES) with an integrated environmental studies program prepared for high school students in the Canadian context. Our research used a retrospective case study design to investigate how aspects of this unique learning environment are related to long-term, active citizenship outcomes as perceived by students from two previous student cohorts (N = 24 and N = 36) who were contacted several years after the culmination of the program. To access information about student perceptions, PLACES was implemented as part of a range of mixed methods which also included focus groups and interviews. This study is important because it links key aspects of the learning environment to long-term citizenship outcomes and is unique in that the data were collected five and eight years later as part of a longitudinal study. Our findings demonstrate that the learning environment and citizenship outcomes were closely linked, and that students’ perceptions as measured by the PLACES instrument (past and present) were remarkably stable across all dimensions. These findings further indicate significant and positive implications for future learning environments research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".