Choosing and Using Citation and Bibliographic Database Software (BDS)
Bibliographic record
Abstract
The diabetes educator/researcher is faced with a proliferation of diabetes articles in various journals, both online and in print. Keeping track of cited references and remembering how to cite the references in text and the bibliography can be a daunting task for the new researcher and a tedious task for the experienced researcher. The challenge is to find and use a technology, such as bibliographic database software (BDS), which can help to manage this information overload. This article focuses on the use of BDS for the diabetes educator who is undertaking research. BDS can help researchers access and organize literature and make literature searches more efficient and less time consuming. Moreover, the use of such programs tends to reduce errors associated with the complexity of bibliographic citations and can increase the productivity of scholarly publications. The purpose of this article is to provide an overview of BDS currently available, describe how it can be used to aid researchers in their work, and highlight the features of different programs. It is important for diabetes educators and researchers to explore the many benefits of such BDS programs and consider their use to enhance the accuracy and efficiency of accessing and citing references of their research work and publications. Armed with this knowledge, researchers will be able to make informed decisions about selecting BDS which will meet their usage requirements.
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 | 0.036 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.050 | 0.071 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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".