Place meaning, speculation, and emerging public perceptions of carbon-storing marine sediments in Dundalk Bay, Ireland
Bibliographic record
Abstract
The natural capacity of marine sediments to capture, sequester, and store organic carbon has been recognized by researchers and policy makers for its potential to mitigate against climate change. As a result, Marine Spatial Planning (MSP) and Marine Protected Area (MPA) designation processes increasingly aim to protect “blue carbon” stored in marine sediments by reducing anthropogenic activities that disturb the seabed (e.g., bottom trawling). In this research, we engaged with coastal residents around Dundalk Bay, Ireland to explore public perceptions of the presence and management of carbon-storing marine sediments in the context of the multifaceted relationship between communities and the environment. This has not been previously studied in an empirical setting. Given the largely “unknown” character of this source of blue carbon, we theorized that speculation played a key role in sustaining emerging perceptions of the sediments, by creating a link with existing place meanings. We used interviews (n = 12) and a focus group (n = 7). Reflexive thematic analysis of the data showed that local residents associated multiple, overlapping meanings with Dundalk Bay. We found evidence that speculative mechanisms such as analogy and experiential knowledge were used to bridge between existing place meanings and emerging perceptions of carbon-storing marine sediments, which also helped indicate the valence of people’s feelings about the sediments. We found different views about the presence of the sediments, and residents varied in their prioritization of measures to protect either nature or economic activity in the bay. Because of scientific knowledge gaps related to the distribution and character of marine sediments and the impacts of anthropogenic activity, participants stressed the need for further research and a careful approach to the management of the bay and its sediments. Our work reiterates the importance of recognizing existing people–place connections to understand potential responses to changes in the use and/or management of marine environments. This can help achieve a more engaged and socially acceptable MSP process.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".