Mental models research to inform community outreach for a campus recycling program
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop a better understanding of the state of knowledge of students and faculty on the Michigan State University (MSU) campus; identify relevant gaps in knowledge and misconceptions about recycling; and provide recommendations regarding how these gaps and misconceptions may be addressed through education and outreach. Design/methodology/approach Using mental models analysis, the current state of knowledge possessed by students and faculty was compared with a comprehensive inventory of on‐campus recycling procedures and opportunities. Findings By combining data from individual mental models elicited from students and faculty members, an overall mental model that depicted the frequency with which subjects understood MSU‐specific recycling concepts was developed. This composite model, and the accompanying statistical analysis, revealed important gaps – on part of both students and faculty – in understanding for several key recycling concepts that are relevant to established campus‐based waste reduction practices. Originality/value The mental models approach, which to the authors' knowledge has yet to be applied to campus sustainability initiatives, provides program managers and outreach specialists with a constructive and transparent opportunity to develop and deploy program information that builds on existing knowledge while also meeting the new information needs of key stakeholders.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".