Do we trust ourselves? Is the human the weak link?
Bibliographic record
Abstract
Generative artificial intelligence tools are becoming ubiquitous in applications across personal, professional and educational contexts. Similar to the rise of social media technologies, this means they are becoming an embedded part of people's lives, and individuals are using these tools for a variety of benign purposes. This article examines how existing information literacy understandings will not work for artificial intelligence literacy, and provides an example of artificial intelligence searching, demonstrating its shortcomings. Present approaches may fall short of the answer required to navigate these new information tools, and this begs the question of what comes next. The current scope of information literacy and technology necessitates a multidisciplinary approach to solving the question of ‘what to do with artificial intelligence’ and arguably most impactfully requires one to acknowledge that what has worked may no longer suffice.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.099 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".