Successful Web Survey Methodologies for Measuring the Impact of Networked Electronic Services (MINES for Libraries)
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
MINES for Libraries is a web-based survey methodology that is proving to be a valid and reliable method for assessing networked electronic resources usage. The methodology has collected usage data on the libraries’ electronic resources, including electronic journals, electronic books, databases, the online catalog, and services such as interlibrary loan. It can also integrate data on non-subscription resources such as digital collections, open access journals, pre-print and post-print servers, and institutional repositories. This web survey method is more successful in libraries that have implemented a network assessment infrastructure. To illustrate its utility, an overview of the methodology, a discussion of assessment infrastructures, and recent results from MINES for Libraries surveys at more than 30 North American universities during the last 2 years are presented, including health sciences libraries, main academic libraries, and a Canadian library consortium of colleges and universities.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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