Bibliographic record
Abstract
Poster presented at DH2017, Montréal, Canada (8-11 August 2017). Poster design by Lindi Melse and Max Kemman. Abstract - Digital history is concerned with the incorporation of digital methods in historical research practices. Thus, digital history aims to use methods, concepts, or tools from other disciplines to the benefit of historical research, making it a form of methodological interdisciplinarity. This requires expertise of different facets, such as technology, history, and data management, and as a result many digital history activities are a collaboration of professionals and scholars from different backgrounds. Such collaborations would fit Svensson’s characterisation of digital humanities as a fractioned trading zone. Simply stated, this means first that digital humanities functions as heterogeneous collaborations, i.e. with participants from different backgrounds, and second that the participants act voluntarily. In this paper, we will investigate these two aspects in the context of digital history to understand how digital history projects function as heterogeneous collaborations, and what the participants’ incentives are for entering such collaborations. We will discuss this by presenting findings from interviews with practitioners in digital history projects, and reflections on projects in which the author himself has participated.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".