A tale of two cities: implications of the similarities and differences in collaborative approaches within the digital libraries and digital humanities communities
Bibliographic record
Abstract
In addition to drawing upon content experts, librarians, archivists, developers, programmers, managers, and others, many emerging digital projects also pull in disciplinary expertise from areas that do not typically work in team environments. To be effective, these teams must find processes—some of which are counter to natural individually oriented work habits—which support the larger goals and group-oriented work of these digital projects. This article will explore the similarities and differences in approaches within and between members of the Digital Libraries (DL) and Digital Humanities (DH) communities by formally documenting the nature of collaboration in these teams. While there are many similarities in approaches between DL and DH project teams, some interesting differences exist and may influence the effectiveness of a digital project team with membership that draws from these two communities. Conclusions are focused on supporting strong team processes with recommendations for documentation, communication, training, and the development of team skills and perspectives.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.030 | 0.039 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".