Toward an integrated information science
Bibliographic record
Abstract
Abstract More than ever, Information Science needs a coherent, powerful, integrated vision of itself and the value proposition it delivers in this Information Age. Complex and multifaceted uncertainties like pandemics and climate change do not yield to narrow or piecemeal solutions. Holistic visions of Information Science existed at our field's formation a century ago, but sadly over the years have become increasingly fragmented and specialized. This 90‐minute panel invites participants in the ASIS&T 2020 Annual Meeting to reconsider the benefits of holism and creates a stage to re‐imagine a big and integrated Information Science. To that end, we will first adopt Bates' (2002) conception of seven interpenetrating “Layers of Understanding.” Then, information scientists with expertise in one of the seven layers will speak about the information phenomena at their level. Having the full range of strata illuminated, Dr. Marcia J. Bates will share her sage reflections. Ample time will remain for the audience to tinker with, extend, challenge, or celebrate the idea of a wide‐ranging, integrated Information Science.
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 | 0.050 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.032 | 0.046 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".