Bibliometrics as a Performance Measurement Tool for Research Evaluation: The Case of Research Funded by the National Cancer Institute of Canada
Bibliographic record
Abstract
As bibliometric indicators are objective, reliable, and cost-effective measures of peer-reviewed research outputs, they are expected to play an increasingly important role in research assessment/management. Recently, a bibliometric approach was developed and integrated within the evaluation framework of research funded by the National Cancer Institute of Canada (NCIC). This approach helped address the following questions that were difficult to answer objectively using alternative methods such as program documentation review and key informant interviews: (a) Has the NCIC peer-review process selected outstanding Canadian scientists in cancer research? (b) Have the NCIC grants contributed to increasing the scientific performance of supported researchers? (c) How do the NCIC-supported researchers compare to their neighbors supported by the U.S. National Cancer Institute? Using the NCIC evaluation as a case study, this article demonstrates the usefulness of bibliometrics to address key evaluation questions and discusses its integration, along complementary indicators (e.g., peer ratings), in a practice-driven research evaluation continuum.
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 | 0.101 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.067 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".