E‐commerce in libraries. Sponsored by SIG LT
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
Abstract E‐commerce is becoming ubiquitous but what does it mean for libraries? Some would argue that in strict terms, libraries have been involved in e‐commerce for years with the marketing of services and resources through web pages. Others would argue that libraries are just beginning to enter the world of e‐commerce with forays into offering ways for library users to pay fines or buy copies of photographs or other items online. Participants will learn about the different ways e‐commerce can be interpreted and how libraries are implementing e‐commerce initiatives. Another side of e‐commerce in libraries is the companies delivering products and services to libraries. Delivering materials to libraries using e‐commerce methods has some benefits and some drawbacks to previous methods. An additional feature to be considered is the increased use of credit cards which speed the process. On the other hand the need to work with consortia and hierarchical or group buying of other sorts brings new complexity. MediaSleuth enables the purchase of non print educational media and the accompanying MARC cataloging over the web. Participants will also learn about the design considerations to enable electronic selection, purchase and delivery of these materials using the internet that one such company uses.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.006 |
| 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