The Time Has Come... To Build, Reflect, and Analyze Connections Between Qualitative and Quantitative Data
Bibliographic record
Abstract
This paper will address the development process of a qualitative evaluation tool to aid in the thorough analysis of library resources at the University of Maryland. Specifically, our project looks at the use and added value of this tool for the building, reflecting, and analyzing the connections between qualitative and quantitative data. This will allow for more meaningful justifications of budgetary decisions compared to cost and use metrics alone. Given the necessity for meticulous review of continuing resources, our project addresses a request for enhanced transparency from the university faculty and library oversight bodies and serves as a useful tool for accountability and justification of impactful decisions for stakeholders internally and externally. We will discuss the extant literature and the need for this type of tool, the development process including the output planning and data input format, the initial reception of the project, and future goals and planning for our initial usage. Additionally, we will demonstrate the use of the tool, model output, and discuss options for visualizations, storage, and retrieval of input data.
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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 | 0.292 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".