Rivers and streams in the media: a content analysis of ecosystem services
Bibliographic record
Abstract
Although ecosystem services research has become common, few efforts are directed toward in-depth understanding of the specific ecological quantities people value. The theoretical framework of final ecosystem services focuses attention on such measurable attributes, as a common currency for social-ecological systems research. Environmental communications as well as ecological monitoring and analysis efforts could be enhanced through increased documentation of final ecosystem services. For example, small changes in the way ecosystems are described could strongly influence relevance to the public and improve the foundation for environmental decision making. Focusing on rivers and streams, we conducted a content analysis of existing publications to document the breadth and frequency with which various measurable attributes, such as flooding, water quality characteristics, and wildlife appeared in different news sources over a multiyear timeline. In addition to attributes, motivations for human interest in river-related resources were also coded, such as recreation or preservation for future generations. To allow testing of differences between materials written for different audiences, three sources were sampled: a blog hosted by National Geographic, New York Times articles, and Wall Street Journal articles. The coding approach was rigorously tested in a pilot phase, with measures developed to ensure high data quality, including use of two independent coders. Results show numerous similarities across sources with some notable differences in emphasis. Significant relationships between groups of attribute and motivation codes were also found, one outcome of which is further support for the importance of nonuse values for fish and wildlife. Besides offering insight on ecosystem services, the project demonstrates an in-depth quantitative approach to analyzing preexisting qualitative data.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".