Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective - To describe the key elements of cost effectiveness analysis (CEA) and demonstrate how such analysis may be used in the library environment. Methods - The paper uses a step by step approach to walk the (non-economist) reader through the basics of conducting a cost effectiveness study. The key elements of a CEA are outlined using examples that illustrate how the analysis may be carried out in the library sector. A case study of a CEA in a hospital library is presented. The case study compares two library services, mediated searching and information skills training, to illustrate the application of CEA and highlight some of its limitations. Results - CEA is a comparative analysis; its key elements include a study question that includes both costs and effectiveness; justification of the perspective the study; evidence of the effectiveness; comprehensive identification of all relevant costs and appropriate measurement of costs and effectiveness. Conclusions - CEA enables comparison of services or interventions in terms of their costs and how effective they are. The results can be used to aid decision-making about service provision.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.143 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it