Bibliographic record
Abstract
Purpose The purpose of this paper is to present how, in May 2008, the Ad Hoc Committee on Federated Search was formed to prepare a preliminary report on federated searching for a special meeting of Librarians Academic Council at Memorial University Libraries. The primary purpose is to discuss current implementation of federated searching at this institution, explore what other institutions have done, examine federated search technologies, and offer recommendations for the future of this resource. Design/methodology/approach Information was drawn from a recent usability study, an informal survey was created, and a literature/technology review was conducted. Findings These four recommendations were proposed and unanimously accepted: actively develop the current federated search implementation by developing a web presence supporting “federated search in context”, re‐evaluating the need for consortial purchase of a federated search tool, continuing to assess the current federated search marketplace with an eye to choosing a next‐generation federated search tool that includes effective de‐duping, sorting, relevancy, clustering and faceting, and that the selection, testing, and implementation of such a tool should involve broad participation from the Memorial University Libraries system. Originality/value Provided is an inside look at one institution's experience with implementing a federated search tool. The paper should be of interest to anyone working in academic libraries, particularly the areas of administration, public services, and systems.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.677 | 0.598 |
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".