Articulation Work Supporting Information Infrastructure Design: Coordination, Categorization, and Assessment in Practice
Bibliographic record
Abstract
Articulation work is a critical factor in information infrastructure building projects that involve multiple and diverse communities. It brings awareness of language differences, ramifications of definition and use of categories, as well as other coordination mechanisms. Articulation work is of particular relevance to scientific endeavors that have broadened in the past decade to encompass global scale research and now require collaborative arrangements to handle complex interdependent elements. Our interdisciplinary research team joined with the long term ecological research (LTER) community of information managers recently to develop articulation work in selected activities. Through this partnership, initial activities and approaches in the articulation process are considered along with language and category uses pertinent to four main concepts (infrastructure, representation, design and mediation). Coordination mechanisms developed and employed over the last year as means for articulation work include dialogue mediation, co-design activities, and category elaboration in addition to traditional and emergent forms of assessment
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 | 0.103 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".