Measuring the effectiveness of Parks Canada's environmental management system: a case study of Riding Mountain National Park
Bibliographic record
Abstract
In 1996, the Geneva‐based International Organization for Standardization released its ISO 14001 guidelines for environmental management systems (EMSs). By implementing an EMS, an organization is better situated to manage the environmental effects of its operations which, in turn, should lead to better environmental performance. However, research on EMS performance has only recently begun to emerge, and the relation between EMSs and genuine improvement in environmental performance has not been clearly established, particularly for organizations such as Parks Canada, whose principal mandate is to protect the natural environment. While EMSs are gaining recognition amongst parks as a systematic approach for dealing with the environmental aspects of park operations, there has been very little investigation as to the effectiveness of EMSs in improving the environmental performance of park operations. This paper presents the results of a case study of the effectiveness of Riding Mountain National Park's (RMNP's) EMS and its contribution to environmental improvement. The results confirm EMS experience elsewhere in that RMNP's EMS has been only moderately successful at best and that there exists no clear link between the EMS and the environmental improvement of park operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".