Keynote 2: Developments in Education for Information: Will “Data” Trigger the Next Wave of Curriculum Changes in LIS Schools?
Bibliographic record
Abstract
The first university-level library schools were opened during the last quarter of the 19th century. The number of such schools has gradually increased during the first half of the 20th century, especially after the Second World War, both in the USA and elsewhere. As information has gained further importance in scientific endeavors and social life, librarianship became a more interdisciplinary field and library schools were renamed as schools of library and information science/ information studies/ information management/information to better reflect the range of education provided. In this paper, we review the major developments in education for library and information science (LIS) and the impact of these developments on the curricula of LIS schools. We then review the programs and courses introduced by some LIS schools to address the data science and data curation issues. We also discuss some of the factors such as "data deluge" and "big data" that might have forced LIS schools to add such courses to their programs. We conclude by observing that "data" has already triggered some curriculum changes in a number of LIS schools in the USA and elsewhere as "Data Science" is becoming an interdisciplinary research field just as "Information Science" has once been (and still is).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.009 |
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".