Knowledge map of Information Science
Bibliographic record
Abstract
This collective paper incorporates eleven position papers on implications of the "Knowledge Map of Information Science,” a Critical Delphi study conducted in 2003-2005 and published as a series of four articles (ZINS, 2007 a, b, c, d). The Delphi study captured the deliberations of 57 leading information science scholars from 16 countries to provide (1) definitions of the fundamental concepts of data, information knowledge and message, (2) alternative conceptions of the broad information science domain, (3) different classificatory mappings of the field, and (4) comprehensive mappings of information science. Overall, the Knowledge Map provides an early 21st century snapshot of the field that should help guide future research, educational programming, publishing, and other professional and scholarly thrusts. Future information science mapping research should be done periodically, including additional Delphi studies and assessments of the degree of the field’s expansion and probable division into sub-fields. Alternative methodologies for mapping the expanding information science universe and its synergies with other fields of knowledge should also be explored.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".