Toward a template for systematic reference and instruction programme analysis
Bibliographic record
Abstract
Purpose To develop a method of systematically analyzing reference and instruction programmes at academic libraries so managers of such services can identify potential areas of change and make more confident recommendations. Design/methodology/approach The paper reviews the library literature and then introduces a template for programme analysis. A case study is used to help illustrate the need for programme analysis, and also to clarify the template. Findings The reference and instruction literature on assessment and new service models indicates that academic librarians are struggling to update programming in order to meet the needs of current library patrons. There is no how‐to manual for managers of reference and instruction departments to analyze their services comprehensively and to decide what changes to make. This paper introduces a template that academic librarians could use to systematically analyze their reference and instruction programming with regard to the history of the programmes, internal and external environmental factors that affect the provision of service, and how the current service model compares with others. Practical implications The use of this template will allow academic librarians at any size library to investigate the historical and environmental factors that affect their services, so they can more confidently identify potential areas of change and make documented and supported recommendations to library administration. Originality/value This paper fulfils a gap in the literature and offers a guide to programme analysis for managers of reference and instruction departments.
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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 | 0.212 | 0.301 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| 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".