Bibliographic record
Abstract
This issue of the Bulletin is largely concerned with the interaction between information systems and information use, as opposed, for example, to articles about institutions or information policy. The core of the issue, though diverse in itself, is our special section, “Vocabularies in Practice” which consists of four papers based on presentations from DC-2005, the 2005 International Conference on Dublin Core and Metadata Applications that addressed the same topic. These papers cover a range of topics from Web services for accessing knowledge organization tools to particular applications. The editor is very grateful to Thomas Baker and Eva Mendez for identifying possible short papers from the conference and contacting the authors. Hopes that we can create systems (solutions) that do IR for us are unreasonable. Expectations that people can find and understand information without thinking and investing effort are unreasonable. Therefore, we aim to develop systems that involve people and machines continuously learning and changing together…Thus, the more theoretical aim of our HCIR [human computer information retrieval] research is rooted in the view that information interaction is a core life process. It is as important to life in an informated age as food and personal relationships. Stephanie Haas and her fellow panelists focus on a narrower but related topic: help. Her paper reports the presentations and discussions of panel members at the 2005 ASIS&T Annual Meeting in Charlotte eager to promote improvements in that perennial source of frustration. In the last of these discussions Karl Fast, in the IA Column, reflects on the role of research in IA – what it is and is not – and its role at the recent 2006 IA Summit in Vancouver and on the inclusion of such presentations and discussions at future IA Summits. Finally, we catch up a bit in “What's New?” with abstracts of papers from JASIST appearing in v.57, issues 3–6.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.300 | 0.235 |
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".