Expanding infrastructure ontologies: Integrative and critical insights for coastal studies and governance
Bibliographic record
Abstract
Diverse knowledge insights are essential to inform action on bringing about transformations in how societies live with changing Earth, ocean and coastal systems. However, knowledge forms typically used in governance systems are stubbornly limited. This paper analyses the extent to which an expanding ontology of the concept of ‘infrastructure’ can contribute to building more integrated knowledge for governance in ocean and coastal contexts. This paper asks: What can creative and critical engagement with infrastructure thinking offer to efforts to bring together diverse forms of knowledge and to develop more effective and ethical governance in changing coastal contexts? Employing a qualitative assessment of how the concept of infrastructure is defined in multiple disciplines and contexts, the paper identifies three heuristic types of structures, things and processes that can collectively inform interdisciplinary dialogue and governance dialogue: (i) built/physical infrastructure, (ii) environmental infrastructure, and (iii) societal/cultural infrastructure. Drawing on insights from critical infrastructure studies and more-than-human perspectives, the paper then identifies ontological and methodological challenges of integration, values, and power/agency for those who engage a multi-faceted conception of infrastructure to frame analysis and action. Bringing these insights together, the paper argues that infrastructure thinking provides a means to facilitate interdisciplinary dialogue, and a useful lens with which to analytically integrate diverse forms of knowledge about/in ocean and coastal contexts. However, cautious and critical perspectives are needed to support efforts in (re)thinking and integrating for collective action and governance.
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.005 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".