Bibliographic record
Abstract
Purpose To challenge the deliberate strategy approach of the environmental management system (EMS) cycle, and offer a model based on both the practical reality experienced at Dalhousie University and emergent strategy theory. Also, to share some of the lessons learned in the 15 years of environmental management at Dalhousie University. Design/methodology/approach A case study of environmental management at Dalhousie University between 1990 and 2005 was conducted. Data were collected through 13 interviews with senior management and through 22 interviews with faculty, students and staff. Findings Two EMS cycles emerged with an overlap in the policy, planning and implementation phases, as well as unpredicted “maintaining implementation” and “renewing” phases. Emergent plans and best practices from other universities fed into the EMS cycle at the implementation and review stages, respectively. Practical implications An improved EMS model is presented. It includes feedback loops, emergent plans, unrealized plans, the renew concept, and best practices. Six practical lessons extracted from the case study are: “early movers”; champions; administrative versus academic versus joint environmental policies; opportunism; and, the change cycle. The sustaining and renewing phases that Dalhousie University experienced are important for practitioners to be aware of. The case itself also presents an overview of numerous initiatives. Originality/value Integrates strategic management and campus EMS theory to create a new model, while also outlining 15 years of environmental management and change cycles experienced at Dalhousie University.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".