This Librarian’s Journey of Testing New Search Innovations: From Retrieval to Artificial Intelligence (AI)
Bibliographic record
Abstract
This article is the author’s personal journey of almost 50 years in adapting to and influencing technology change in the field of libraries of all types. He shares his learning and perspectives on the journey and how we often look at new technologies through the lenses of the past. We then mature through playing with and experiencing the new innovations and seeing the opportunities more clearly. His goal is to use the historical lens to inform our discussions and adoptions of current and future technologies such as the range of artificial intelligence applications that are emerging at rapid speed. He includes a glossary of the major technological adaptations and adoptions in libraries from an historical view and the current opportunities in AI, LLMs, agents, and more.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.041 | 0.044 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".