The International Seabed Authority, the Problem of Disregard and the Case for Administrative Accountability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The International Seabed Authority (ISA) is tasked with regulating deep seabed mining (DSM) in areas beyond national jurisdiction for the benefit of all humankind. Unlike most international institutions, the ISA operates as a frontline resource regulator with direct authority over DSM contracts and activities. To effectively carry out its regulatory mandate, the ISA operates under a complex institutional structure involving the delegation of significant powers to non-plenary bodies and administrative actors. As decision-making shifts to bodies less directly linked to State consent, it becomes increasingly important to ensure that these actors remain accountable both to the States granting them authority and to those affected by their decisions. This article argues that there is a mismatch between the ISA’s decision-making structure and its systems of administrative accountability that lead to a problem of affected interests being disregarded. The article highlights the structural and practical barriers that lead to this and then turns to an examination of the process mechanisms that the ISA has put in place to ensure that its decisions are responsive to affected interests. Whilst the ISA has some positive ad hoc procedures in place, it does not consistently institutionalise core administrative law pillars such as transparency, meaningful consultation, and the opportunity for review of decisions. A challenge for the ISA is to identify the range of accountability relationships created by the DSM regime, and to develop clear and consistent standards of accountability that can address the problem of disregard.
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.
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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it