Mapping Change in the Science of Ocean Change
Bibliographic record
Abstract
Using bibliometric analysis techniques, we trace the evolution of climate and climate-change related articles in major oceanographic journals, 1987-2017. We use these bibliometric tools (network mapping, cluster analysis, alluvial analysis, corpus keyword detection) to document trends in growth, integration and centralization of climate-related research within ocean sciences over the past three decades. Such analysis methods offer an objective and complementary methodology, in contrast to the traditional “expert panel” approach, for guiding long-term strategic science planning. But how does the macro trend compare to scientific outputs supported by large ocean observatory facilities? Have scientists making use of these facilities followed, led or diverged from the general trend? We compare the macro trend to corpora of published science from two such facilities, Australia’s Integrated Marine Observing System (IMOS) and Ocean Networks Canada (ONC). The goal is to discern the extent to which these “big science” ocean observatories have been able to support or lead research that helps inform policy, management and the public about critical societal issues such as long term ocean change.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.049 | 0.077 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".