“Unity in Diversity”: A Conversation around the Interdisciplinary Identity of Information Science
Bibliographic record
Abstract
Abstract As a dynamic and interdisciplinary field of study, information science has a diverse set of methods, theoretical frameworks, tools and processes that continue to be developed, adopted, and extended through further research, teaching, and practice. Some of the methods and frameworks have origins in other disciplines. The interdisciplinary nature of information science may have enabled the field to grow in stature but it may have also contributed to it being perceived, often unfairly and mistakenly, as lacking a strong identity, brand, and reputation, leading to a possible fragmentation of the field. Continued conversations around factors that may help and/or hinder the field from fulfilling its full potential and how it can position itself to build an identity on a strong track record are necessary. The panel has two main goals: (1) to engage researchers and educators in an interactive discussion on the contributing factors and ways in which information science can remain a diverse and interdisciplinary field, realize its full potential, and build a strong identity as well as identify potential barriers it needs to overcome; and (2) to delineate the roles its stakeholders and allies need to play to achieve that goal of a field with “Unity in diversity”.
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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 | 0.132 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.087 | 0.087 |
| Scholarly communication | 0.040 | 0.049 |
| Open science | 0.005 | 0.048 |
| Research integrity | 0.025 | 0.064 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".