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
The papers in this issue developed out of presentations at the 2007 Digital Humanities Conference, at the University of Illinois, in Champaign-Urbana. A conference issue is a regular feature of Literary and Linguistic Computing, but this issue is a first, of sorts, since the articles it includes were chosen in a kind of plebiscite. Immediately after the conference concluded, we used the online conference registration and management system (Conftool, developed by Harald Weinrich) to email all conference participants and ask them what they thought were the best, most interesting papers they had heard. Their nominations guided the editors of this issue (the program chair, Ray Siemens, and the local host, John Unsworth) in selecting and assembling this issue. Indeed, it is evidence of the confluence of ideas in our community that these nominations produced a collection of articles that is so thematically unified. In ‘Thinking about Interpretation', John Bradley discusses his work to build tools that extend the ability of scholars in the humanities to do what they really want to do, namely ‘study texts by reading them' (quoting Claire Warwick from the Companion to Digital Humanities). Bradley's software package, called Pliny, is a tool of this sort, ‘named after the classical author Pliny the Elder who was well known in his own day as someone who was constantly writing notes about things he was interested in.' Pliny, the software, assumes that you might want to annotate and think about any kind of digital object—an image, a web page, a text file, an audio file, etc. It also supports interpretation, as a kind of emergent structure, by allowing arrangement, naming, grouping, and typed reference.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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