Mapping the predicted and potential impacts of metal mining and its mitigation measures in Arctic and boreal regions using environmental and social impact assessments: a systematic map protocol
Bibliographic record
Abstract
BackgroundSince the 1960s, environmental impact assessments (EIAs) and recently, social impact assessments (SIAs), have been conducted during the planning stages of large development projects to identify potential adverse effects and propose mitigation measures to ameliorate these impacts. EIAs and SIAs should outline all possible positive and negative effects of a proposed action or development on ecological and social systems respectively, including biodiversity, flora and fauna, abiotic components (such as air quality), human health, security and wellbeing. The work outlined herein aims to generate a list of all possible direct and indirect effects of metal mining (including gold, iron, copper, nickel, zinc, silver, molybdenum and lead) along with the impacts of mitigation measures proposed, that are mentioned in EIAs and SIAs for action in Arctic and boreal regions of the following countries/regions: Canada, Alaska (USA), Greenland, the Faroe Islands, Iceland, Norway, Sweden, Finland and Russia. MethodsWe will conduct searches for environmental and social impact assessments in Swedish and English, and until theoretical saturation is reached (i.e. no new action-impact pathways are identified). We will perform searches of specialist websites (e.g. public repositories of environmental and social impact assessments) and Google Scholar. We will also contact relevant stakeholders (that have been identified in the wider 3MK project https://osf.io/cvh3u/) and make a call for additional information. Eligibility screening will be conducted at two levels: title and full text. Meta-data will be extracted from eligible studies including type of mining activity, location of mine, type of impacts, and planned mitigation measures. Findings will be presented narratively, in a searchable relational database and in an Evidence Atlas (a cartographic map). We will produce a framework of different mining impacts and related mitigation measures from practitioners’ knowledge reflected in EIAs and SIAs.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".