Discombobulated Actor-Networks in a Maritime Resource Frontier
Bibliographic record
Abstract
Papua New Guinea’s first deep-sea mining project, once touted as the first of its kind in the world, now appears to be “dead in the water.” The mining company behind it has been liquidated, the mining equipment has been rendered obsolete, and the host government has been made to look foolish for supporting the enterprise. This paper examines the application of two concepts—that of the “resource frontier” and that of the “actornetwork”— to reach an understanding of the history of this apparent failure. By elaborating on the additional concept of a “network junction,” it seeks to show how arguments about the feasibility or fallibility of this particular project, and deep-sea mining proposals more broadly, have been related to arguments about a range of other issues in which scientific and technological uncertainties are associated with environmental and social impacts or environmental and political risks. Instead of seeking to explain the failure of this project by reference to the attributes of a specific type of maritime resource frontier, the paper shows how the articulation of different policy networks creates the appearance of a frontier in which human and nonhuman actors have combined to produce a variety of unpredictable and open-ended outcomes. From this point of view, the history of this project’s failure cannot simply be read as the outcome of a contest between two groups of human actors with clearly defined interests or ideologies, nor does it necessarily spell the end of the policy network in which this project has been embedded.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".