Evaluating Bibliographic Referencing Tools for a Polytechnic Environment
Bibliographic record
Abstract
Abstract Objective – This paper analyzes the design process for a toolkit for appraising emerging and established bibliographic reference generators and managers for a particular student population. Others looking to adapt or draw from the toolkit to meet the needs of users at their own institutions will benefit from this exploration of how one team developed and streamlined the process of assessment. Methods – The authors implemented an extensive initial evaluation using a checklist and comprehensive rubric to review and select reference tools. This work was guided by a matrix of categories from Marino (2012), Bates (2015), and other literature. As the tools were assessed using the toolkit, the components of the toolkit were evaluated and revised. Toolkit revisions were based on evaluators’ feedback and lessons learned during the testing process. Results – Fifty-three tools were screened using a checklist that reviewed features, including cost and referencing styles. Eighteen tools were thoroughly evaluated using the comprehensive rubric by multiple researchers to minimize bias. From this secondary testing, tools were recommended for use within this environment. Ultimately the process of creating an assessment toolkit allowed the researchers to develop a streamlined process for further testing. The toolkit includes a checklist to reduce the list of potential tools, a rubric for features, a rubric to evaluate qualitative criteria, and an instrument for scoring. Conclusion – User needs and the campus environment are critical considerations for the selection of reference tools. For this project, researchers developed a comprehensive rubric and testing procedure to ensure consistency and validity of data. The streamlined process in turn enabled library staff to provide evidence based recommendations for the most suitable manager or generator to meet the needs of individual programs.
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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 | 0.165 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".