Bibliographic record
Abstract
The term community of practice (CoP) has been applied to segments of work in the digital humanities in numerous ways over the years: as library training initiatives (Green 2014), as work around a specific encoding practice (Flanders and Jannidis 2015), and even to the DH community as a whole (Siemens 2016). This term, coined in 1991, was originally applied to learning, which the authors claimed was a "sociocultural practice" (Lave and Wenger). It has been further developed by Wenger (2011), who defines it as follows: "Communities of practice are groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly." In this panel, we use this latter definition as a framework for reflecting on the first year of work in the Linked Infrastructure for Networked Cultural Scholarship (LINCS) Project.
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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".