Data from: Popular press portrayal of issues surrounding free-roaming domestic cats (Felis catus)
Bibliographic record
Abstract
This dataset is comprised of variables coded/extracted from popular press articles about domestic cats (Felis catus), which were evaluated as part of a media-content analysis. Our focus was understanding how a number of issues surrounding free-roaming (feral) cats are presented and discussed in the popular press, including: - The messengers who are quoted or referenced (e.g., cat advocates, veterinarians, naturalists, researchers) - The risks and threats to which feral cats are exposed (e.g., diseases, vehicles, predation)- The impacts feral cats have on the environment, native wildlife (e.g., via predation), and threats they pose to human health (e.g., via disease transmission)- The potential strategies and tools used to manage feral cat populations and their impacts (e.g., trap-neuter-release, bylaws, public education) We used the Lexis Nexus search engine to conduct a systemic search for English-language popular print media, including news articles and bulletins, opinion-editorials, and other public notices (e.g., classifieds) published between 1990 and 2018 (see Search Terms in READ_ME file and Methods: Search in the referenced article). Using a code book we developed (see Questions Coded From Articles in READ_ME), we evaluated each article based on whether they conveyed a variety of different messages. In total, the dataset is comprised of 796 articles, with the bulk (~95%) of articles from the United States and Canada. Most of the people interviewed ("messengers") were from non-governmental organizations, mainly from cat-welfare or cat-rights groups. Researchers, shelter organizations, veterinarians, and groups that differ on how to resolve issues surrounding free-roaming cats were rarely interviewed. Most articles focused on cat welfare issues and the management strategies of euthanasia or trap-neuter-release (TNR), whereas less than one-third of the articles acknowledged that cats have any impact on wildlife or the broader environment. See READ_ME file for a full list of variable definitions.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.043 |
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".