A Design Methodology for Exploring and Communicating System Values and Assumptions
Bibliographic record
Abstract
This paper attempts to make two contributions to discussions related to TEI: (1) an analysis of how tools used for working with TEI documents encourage certain values and make certain assumptions about the work of textual editing and (2) a report on a methodological framework from outside the humanities that suggests a unique way to study such systems. Borrowing models of design research from the fields of design and human-computer interaction, I argue that prototypes can be used to create new conceptual knowledge, to investigate the values and assumptions of sociotechnical systems, and to communicate alternative visions of those systems. I first analyze an existing tool, the Versioning Machine, as a way of focusing the design of a prototype that reimagines several aspects of that original—specifically, I argue that the Versioning Machine creates an environment that to some extent assumes that TEI documents are created by one editor and intended for one instantiation. The prototype presented experiments with an alternative vision of textual editing as bringing encoded texts and interpretations together in multiple and flexible instantiations. Rather than a technical problem with an optimal solution, I approach this design process as an opportunity to ask how prototypes can give designers access to conceptual issues and allow users to enact alternative values and imagine alternative futures. This research was supported by the Modernist Versions Project, which is funded by a Social Sciences and Humanities Research Council of Canada Partnership Development Grant.
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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 | 0.084 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".