Bibliographic record
Abstract
A Review of: Orr, R. H. (1973). Measuring the goodness of library services: A general framework for considering quantitative measures. Journal of Documentation, 29(3), 315-332. Abstract Objective – To discuss the theoretical design of a measure of library quality and value that could be used across functional areas of a library in order to justify and maximize the allocation of resources. Design – This theoretical article provides background on how to conceptualize and develop a quantitative measure of library goodness. Setting – The process delineated is applicable to any library, whether public, academic, or special. Subjects – The intended audience is library management, both at the director and the department head levels. Methods – The author provided examples and questions in the development of appropriate variables. Main Results – The author presented a discussion of potential variables. These variables include library capability and utilization. Conclusion – The article concluded with a discussion of the major desiderata for an effective measure of library goodness: appropriateness, informativeness, validity, reproducibility, comparability, and practicality.
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 | 0.040 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".