Research trends and content analysis of ocean literacy studies between 2017 and 2021
Bibliographic record
Abstract
Ocean literacy (OL) refers to the ability of citizens to understand and explain the concepts and phenomena related to the oceans, and leads them to positive behavioral change for the protection and sustainability of the oceans. The study presents a bibliometric analysis of ocean literacy-based studies published between 2017 and 2021, in order to provide more meaningful information about (a) the academic journals that mostly publish ocean literacy studies, (b) the content analysis of the articles, (c) country rankings over the years (d) the keywords mostly used and (e) the funding source. The Web of Science (WoS) and Scopus databases were used to find ocean literacy-based articles. Seventy-nine articles from forty ocean literacy academic journals covered by WoS and Scopus were carefully selected using predefined criteria. The results revealed that most of ocean literacy-based articles were published in the Frontiers in Marine Science journal (n=23). The countries that published the most ocean literacy-based articles were UK, Italy, Canada, USA, and Portugal. Most of the studies were supported by governmental budgets (n=44). The most popular concepts in ocean literacy-based studies included “Global OL Perspectives’’, “Sustainability”, “Citizen Science”, “Students’ OL Improvement”, “Measuring and Evaluating Students and Teachers’ OL”, “Stakeholders’ Effects on OL”, “OL Based Books-iBooks-Textbooks” and “Individuals’ Affective Domain on OL”. By considering the large number of ocean literacy-based articles published in academic journals indexed in WoS and Scopus, this article can contribute significantly to ocean literacy studies and informed and responsible research, as well as to citizen input to policy development on ocean literacy.
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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.119 | 0.161 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".