Making History: The Pioneers of Information Science Who Made a Difference
Bibliographic record
Abstract
ABSTRACT In recent years, the SIG History and Foundation of Information Science (SIGHFIS) has emphasized the historical scholarship of information science (IS) to foster greater self‐reflexivity in the field. Historical research and reflection can reveal paths not taken or foundational lessons. Early trailblazers made a profound difference in information science—and society. This panel investigates four diverse forerunners’ significant contributions and unique roles in the evolution of the discipline. Panelists will discuss Chinese information scientist Tsien Hsue‐shen and US information scientists Claire K. Schultz, Don Swanson, and Linda Smith to highlight their intellectual legacies for information research and practice in the data intelligence age. The session will include a guided Q&A that challenges the audience to make connections to current problems in IS and to discuss paths that were not taken.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".