Absorbing DiRT: Tool Directories in the Digital Age
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the summer of 2017, Quinn Dombrowski, an IT staff member in UC Berkeley’s Research IT group, approached Geoffrey Rockwell about the possibility of merging the DiRT Directory with TAPoR, both popular tool discovery portals. Dombrowski could no longer offer the time commitment required to maintain the organizational structure of the volunteer-run tool directory (2018). This decommissioning of DiRT illustrates a set of problems in the digital humanities around tool directories and the tools within as academic contributions. Tool development, in general, is not considered sufficiently scholarly and often suffers from a lack of ongoing support (Ramsay & Rockwell, 2012). When tool discovery portals are no longer maintained due to a lack of ongoing funding, this leads to a loss of digital humanities knowledge and history. While volunteer-based directories require less outright funding, managing and motivating those volunteers to ensure that they remain actively involved in directory upkeep requires a vast amount work to ensure long-term sustainability (Dombrowski, 2018). This paper will explore the difficult history of tool discovery catalogues and portals and the steps being taken to save the DiRT Directory by integrating it into TAPoR. In particular, we will: – Provide a brief history of the attempts to catalogue tools for digital humanists starting with the first software catalogues, such as those circulated through societies, and ending with digital discovery portals, including DiRT Directory and TAPoR. – Discuss the challenges around the maintenance of discovery portals – Consider the design and metadata decisions made in the merging of DiRT Directory with TAPoR.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it