Envisioning our information future and how to educate for it: A community conversation
Bibliographic record
Abstract
ABSTRACT Members and others attending the 2015 Association for Information Science and Technology (ASIS&T) conference will be aware of a need to regularly revisit and redefine an information discipline continuously in flux. Constant change likewise demands that we consider new models and approaches to educating professionals equipped with cutting‐edge skills in critical thinking and applied best practice responsive to a dynamic information environment. This proposed three‐segment interactive panel session will report on action research on, and findings emerging from, a re‐visioning of information education. Initial outcomes from pilot projects involving the design and testing of innovative proofs of concept will also be discussed. Attendees will engage in an activity that identifies trends, and debates issues and controversies that are at the core of the dialogue surrounding our information future and how to educate for it.
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 | 0.111 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.070 | 0.040 |
| Scholarly communication | 0.040 | 0.046 |
| Open science | 0.006 | 0.038 |
| Research integrity | 0.029 | 0.063 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".