Using the Kano Model to Display the Association between Percentages of Keywords within Abstracts and Article Citations: A Bibliometric Study for JMIR Journals (Preprint)
Bibliographic record
Abstract
BACKGROUND Academic literature suggests keywords are retrieved from paper’s title and abstract. However, none explored the percentages of keywords within an abstract (PKWA) and investigated the association with the article citations so far. OBJECTIVE We compared the PKWA in journals and investigated the association between PLWA and article citations for scientifically scholar journals. METHODS Selecting 2,796 abstracts and the corresponding keywords from 15 JMIR journals published between 2017 to 2018 on the US National Library of Medicine National Institutes of Health(Pubmed.org), we downloaded the number of citations matched by the PKWA for each article and then investigated the association between them. Choropleth maps were used to present the most productive and cited countries/areas in JMIR journals. The PKWA for each journal was transformed into odds by using the formula(=PKWA/(1-PKWA)) and displayed on a dashboard using the bootstrapping method to compare the differences across journals. The Kano model was applied to interpret the association between PKWA and the article citations. RESULTS The overall PKWA for the 15 JMIR journals is 65.3%. The top three most productive and cited countries are from the United States, the United Kington, and Canada. The differences in PKWA were found among JMIR journals. Only 0.4% of articles with lower PKWA had the tendencies toward a higher number of citations using the Kano diagram to interpret the results. The most influential article with PMID=28663162 published in 2017 has been cited 78 times, but only one keyword “health behavior” that exits in the context. The PKWA is 0.16(=1/6) for this article. CONCLUSIONS The moderate PKWA(=65.3%) urges us to reconsider whether keywords should be(or must be) from the paper’s title and abstract. The effect on searching PubMed for a keyword will be duplicated if the scheme with [All Fields] is applied. The number of keywords in the context can increase the visibility of an article which is a merit good for discusses in the future. CLINICALTRIAL Not available
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 | 0.046 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.033 | 0.048 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".