Good practices for environmental assessment
Bibliographic record
Abstract
Environmental assessment (EA) has emerged in the last five decades as one of the primary management tools that governments use to protect the environment. However, despite substantial theoretical development and practical experience, there are concerns that EA is not meeting its objectives. This article develops a set of good practices to improve EA. An integrated list of proposed good practices is developed based on a literature review of impact assessment research and related fields of study. The practices are then evaluated by surveying experts and practitioners involved in EA of tar sands (also known as oil sands) development in Canada. In all, 74 practices grouped under 22 themes are recommended to improve EA. Key unresolved issues in EA requiring future research are identified.
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 | 0.122 | 0.153 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".