Collect with Intent: Craft Meaningful Questions that Drive Evidence Based Assessment Strategies
Bibliographic record
Abstract
Data analysis is a relatively new skill sets required of librarians. Many articles published over the past several years focused on the fact that training opportunities are not widely available, and this disparity has prevented the standardization of assessment practices within the profession. I propose that the key to developing sustainable assessment strategies is to first uncover the correct questions to guide investigations. The inquiry process provides a focus to assessment work, ensures that the proper data is collected, and dictates how to conduct analysis activities in order to arrive at answers that support collection decisions. When librarians locate the central questions at the heart of evidence-based collection assessment, they create a roadmap that leads to correct answers and essentially, guides efforts to standardize assessment practices across the professional community as a whole.
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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 | 0.354 | 0.452 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".