School Libraries Online: Exploring Issues and Developments Through the IASL Web Site
Bibliographic record
Abstract
In Internet terms, the origins of the Web site of the International Association of School Librarianship are shrouded in the mists of antiquity. Way back at the beginning of 1995, I established IASL-LINK as an Internet listserv for the Association. Dr Jean Lowrie, a founder of IASL and then retiring as Executive Secretary, wanted a home page for IASL. I, too, thought we should have a home page and probably a full Web site. Others doubted that the Association had the resources to do this - or even that it should be done at all. Those of you who know Jean will know that she is persistent, and in this case she focused that persistence on me. I felt that establishing a Web site was a job for a bright young member of the Association, and so I kept hoping that "someone else" would do it. In the end, I lost patience just at the time Jean became very persistent. In November 1995 I created a home page for IASL, with eleven supporting pages, just to prove that it could be done. Thus it was that I found I was the Webmaster.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.032 | 0.038 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".