The Who, Where, and What of Publications in the <i>Journal of Applied Animal Welfare Science</i> from 2009 to 2019: A Bibliometric Analysis
Bibliographic record
Abstract
The aim of this bibliometric analysis was to identify the authors, their institutions and countries, and the content of articles published in the Journal of Applied Animal Welfare Science (JAAWS) from 2009–2019. An analysis of 338 articles identified Emily Weiss, Jason B. Coe, and Emily McCobb as frequent publishers and Pauleen C. Bennett and Terry L. Maple as prolific collaborators. Georgia J. Mason, Kathy Carlstead, and Geoffrey R. Hosey were identified as the most cited authors, whereas Emily J. Bethell had the most cited publication and Jeanne Altmann was the most cited JAAWS reference. Analysis of the organizations from which research published in JAAWS generated revealed the University of Guelph, Purdue University, Tufts University, University of California – Davis, Monash University, and Unitech Institute of Technology as prolific contributors. The top three countries central to JAAWS publications were the USA, Australia, and England. Analysis of the keywords identified animal welfare, welfare, behavior, and dog as salient descriptors. Text analysis of titles and abstracts revealed behavior, effect, time, and dog as key descriptors. Findings are discussed within the broader context of anthrozoological research and literature.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.111 | 0.169 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".