E‐commerce in libraries. Sponsored by SIG LT
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.181 | 0.124 |
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".