Boundary objects in information science research: An approach for explicating connections between collections, cultures and communities
Bibliographic record
Abstract
ABSTRACT Boundary objects (BO) are abstract or physical artefacts that reside in the interfaces between organisations or groups of people. The concept of BO, introduced by Star and Griesemer in an article in 1989, has been used in a broad variety of studies in different research communities from management to computer science and different fields of information science. The aim of this panel, composed of experienced BO researchers, is to provide an overview of and introduction to the state of the art of information science research informed by the theory for the researchers and practitioners participating in the conference; to illustrate the variety of studies and contexts in which the notion of BOs can be found useful in explicating connections between collections, cultures and communities; and to push forward the state of the art of BO‐oriented information science research by discussing new empirical and practical areas of interest and the theory itself.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".