Learning from experience: emerging trends in environmental impact assessment follow-up
Bibliographic record
Abstract
HE HISTORY OF environmental impact assessment (EIA) follow-up is nearly as long as the practice of EIA itself. A large body of work produced in the 1980s was devoted to the topic and this set the scene concerning aims, approaches and techniques for EIA follow-up. A recent upsurge of interest in EIA follow-up has seen it become the topic for a series of workshops at the International Association for Impact Assessment (IAIA) conferences from 1999 to 2005. Many of the findings, deliberations and case studies presented at these workshops and elsewhere have been published in journal articles in recent years. Towards the end of last year we edited a book devoted to EIA and strategic environmental assessment (SEA) follow-up practice, drawing on experiences from around the world (Morrison-Saunders and Arts, 2004). A review of this book by Dr Alan Bond (University of East Anglia) is included in the Book Reviews section of this volume. Having produced this book, we did not think that there was much more to say on the topic. However, a series of papers presented at the 2003 and 2004 IAIA conferences demonstrated an emerging interest and expertise in follow-up in socio-economic matters in particular, as well as further innovations in follow-up of ‘traditional’ project biophysical impacts to include cumulative and health impacts and fledgling conceptualisations of what SEA follow-up might entail. This kindled our interest in editing a special edition of Impact Assessment and Project Appraisal (IAPA) devoted to follow-up, which would explore the latest developments in the field. The world-wide practice of EIA and follow-up is reflected in this special issue, which includes practitioner contributions from Australia, Brazil, Canada, Finland, The Netherlands, Portugal, South Africa and the United Kingdom. The articles in this volume are presented in a sequence that approximately mirrors the evolution of thinking and expertise in the field. In introducing the articles, we summarise some of the key lessons learned from the collective body of wisdom presented and offer some perspectives on future new directions for EIA follow-up, including the notion of follow-up for sustainability assurance. Firstly, though, it is appropriate to take stock of the current state of play and this is the purpose of the first article in the volume.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".