Inside ASIS&T: ASIS&T meeting news
Bibliographic record
Abstract
I Future for All is the theme for the 2006 ASIS&T Annual Meeting, scheduled for November 3-8 in Austin, Texas.Information technology has enabled an expanding digital world, inextricably linked to our physical existence but revolutionary in terms of human creativity and thought.New technologies for mobile communication, massively distributed collaboration and real-time information sharing are radically impacting human expressions, interactions and records.We can anticipate a continuing demand for powerful information organization, aggregation and dissemination tools to harness these new information realities.However, the key to understanding these trends must be found at human and social levels.To reflect on and address the challenges ahead, the organizers of the Annual Meeting hope to focus on some of these questions:What are the implications of these digital trends?What opportunities are arising?Are there dangers that we need to prepare for?How will the future information world be shaped and who will shape it? IA Summit Soarshe 2006 ASIS&T Information Architecture (IA) Summit, held in T Vancouver in March, took the field to new heights with record attendance, stellar speakers and presentations and plenty of time for hallway discussions.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.286 | 0.278 |
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".