A Bibliometric Analysis of Digestive Health Research in Canada
Bibliographic record
Abstract
Measurement of the impact and influence of medical⁄scientific journals, and of individual researchers has become more widely practiced in recent decades. This is driven, in part, by the increased availability of data regarding citations of research articles, and by increased competition for research funding. Digestive disease research has been identified as a particularly strong discipline in Canada. The authors collected quantitative data on the impact and influence of Canadian digestive health research. The present study involved an analysis of the research impact (Hirsch factor) and research influence (Influence factor) of 106 digestive health researchers in Canada. Rankings of the top 25 researchers on the basis of the two metrics were dominated by the larger research groups at the University of Toronto (Toronto, Ontario), McMaster University (Hamilton, Ontario), and the Universities of Calgary (Calgary, Alberta) and Alberta (Edmonton, Alberta), but with representation by other research groups at the Universities of Manitoba (Winnipeg, Manitoba), Western Ontario (London, Ontario) and McGill University (Montreal, Quebec). Female and male researchers had similar scores for the two metrics, as did basic scientists versus clinical investigators. Strategic recruitment, particularly of established investigators, can have a major impact on the ranking of research groups. Comparing these metrics over different time frames can provide insights into the vulnerabilities and strengths of research groups.
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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.108 | 0.279 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".