Bibliographic record
Abstract
Since its inception in 1955, the term “artificial intelligence” (AI) has seen a recent revival with generative AI. But what does this mean for Canadian Library and Information Sciences (LIS) responses? Text analysis was performed in 56 AI articles from nine Canadian LIS journals spanning 1982 to 2024. Using diachronic and sentiment trends, the identified corpus highlights that past familiarity with more traditional AI has led to a balanced and possibly more critical sentiment that provides context, acceptance, and concern for future generative AI technologies within the Canadian LIS landscape. L'IA dans les revues canadiennes de BSI: une analyse de texte RésuméDepuis sa création en 1955, le terme « intelligence artificielle » (IA) a connu un renouveau récent avec l'IA générative. Mais qu'est-ce que cela signifie pour les bibliothèques canadiennes ? Une analyse de texte a été effectuée sur 56 publications rédigées par l'IA, provenant de neuf revues canadiennes en BSI entre 1982 et 2024. En utilisant les tendances diachroniques et de sentiment, le corpus souligne que la familiarité passée avec l'IA plus traditionnelle a conduit à un sentiment équilibré et peut-être plus critique qui fournit contexte, acceptation et préoccupation pour les futures technologies en IA générative sur le plan canadien de la BSI. Mots-clésIntelligence artificielle; analyse de texte; contexte canadien
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.092 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 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".