Loose and Limited Concepts: Using Coauthor Network Analysis to Identify Potential Gaps in the Understanding of Barrier Islands
Bibliographic record
Abstract
Houser, C.; Smith, A.; George, E.; Lehner, J., and Lunardi, B., 2021. Loose and limited concepts: Using coauthor network analysis to identify potential gaps in the understanding of barrier islands. Journal of Coastal Research, 37(4), 873–881. Coconut Creek (Florida), ISSN 0749-0208.Understanding of coastal geomorphology expands through collaborative social networks that are expressed through coauthorship. This technical communication examines the structure of coauthorship networks and research on barrier islands with a focus on nearshore bars, rip currents, swash, beach-dune interaction, foredunes, and the backbarrier. Coauthorship in coastal geomorphology is largely based on regional organizational networks and academic lineages, which may limit the cross-fertilization of ideas and techniques that would allow for an improved understanding of barrier island response to storms and sea-level rise. It is also argued that the lack of collaboration has an influence on field sampling strategies and the development of process-based models and machine learning algorithms to predict coastal barrier evolution that ultimately inform coastal management practices.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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, 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".