Qualitatively recognizing the dimensions of student environmental identity development within the classroom context
Bibliographic record
Abstract
Abstract This study qualitatively explored the process of student environmental identity development (sEID) within the highly social and structured context of elementary school science. Social practice theory was used as the lens to distinguish the dimensions of sEID that were visible during a curriculum‐based, in‐school program focused on the issue of pollution. Student narratives, collected from small group interviews and reflective journals, were prioritized to capture the process of students in context identifying as “being for the environment.” Data collected from 35 grade six students were qualitatively coded, a network diagram was used to visualize the relationships in the data, and a research vignette was constructed. Eight dimensions were recognized as contributing to sEID; the opportunity to be an environmental actor with peers, increased awareness of environmental threat, emotional responses, self‐recognition for environmental action, perceived agency, changed behavior across social contexts, social recognition for identity actions, and personal meaning. While many of these dimensions have been directly or indirectly discussed in the research on adult environmentalists, shifting the emphasis from group membership to the individual student in context led to the addition of two dimensions—personal meaning and emotional responses. Recognizing the eight dimensions of sEID is an important contribution to the literature as students engaging in environmental action as a requirement of school is distinct from the existing research. Identifying the dimensions of sEID can support the intentional design of learning sequences that foster environmental identities in school and beyond.
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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 | 0.004 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".