International News Coverage of Extractive Industries in Indigenous Environments: Factors in News Gatekeeping of Mining Coverage in Scandinavia and Greenland
Bibliographic record
Abstract
Extractive resources derived by the global mining industry are critical to modern existence. Without iron ore, bridges, tunnels, skyscrapers, mass agricultural equipment, telecommunications infrastructure, and transportation – from the shipping and aerospace industries to motor vehicles – would not be possible. Uranium is essential for nuclear energy and nuclear weapons, the most powerful tool of war known to man. Copper, iron ore, nickel, and rare earth minerals are vital to modern society, technology, and communications. Frequently, extractive resources exist in Indigenous lands. Extraction disproportionately affects Indigenous people, due to their connection to the natural environment and traditional livelihoods that rely on the natural world. Yet despite these substantial environmental and cultural implications, the issue of mining and Indigenous people inconsistently makes it on the global news agenda. This thesis explores and deconstructs the possible reasons for this phenomena through specific examination of news gatekeeping, based on qualitative interviews with former and current news editors of the BBC and Washington Post, and as seen in the context of international coverage of mining activity in Sweden and Norway, two central areas of the Indigenous Sámi people, and Greenland, home of the Inuk Inuit. This thesis reveals how multiple News Values, newsroom economics, source crediblilty, and access to Indigneous perspectives and journalists influence coverage decisions.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".