ALISE/ASIST Joint panel on accreditation: Moving forward with LIS accreditation reform
Bibliographic record
Abstract
ABSTRACT The landscape of education in Library and Information Science (LIS) is shifting as the ways in which information is managed and used in society evolves. As these changes happen how we validate our educational programs needs to change as well. Discussion about the ways in which accreditation of LIS education programs is currently conducted has become an increasingly contested issue within the Information community. This panel will examine the current approaches to accreditation, sketch the progress that has been made on accreditation reform, consider what educational institutions and the LIS community are seeking to achieve with accreditation, and conclude by considering the next steps for ASIS&T and the information community as we press forward with efforts to reform accreditation.
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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 | 0.148 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.035 | 0.015 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.083 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".