Bibliographic record
Abstract
This introductory article explores the interconnectedness of the articles in this issue through the lens of artificial intelligence (AI), bots, and other technologies. The articles presented in this issue strive to demonstrate how the library and information science (LIS) field uses AI to interrogate social conflicts, critically question our professional knowledge base, engage in localized community knowledge building, and create interactive maps to preserve cultural knowledge and decentralize Western metadata values in non-Western contexts. This introductory article is presented as a readerly and writerly response to those articles because, as an experiment, I have co-authored this piece using Google’s Bard, a recently released AI chatbot. Google’s Bard is a powerful tool that generates text, translates languages, writes creative content, and answers questions. In this editorial, I share my own experience using Bard to identify the implications of AI as a co-author of the text. I discuss its advantages (if any) and disadvantages, and I outline how AI could impact the future of the LIS field.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.028 | 0.052 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".