Literary texts in an electronic age: Scholarly implications and library services [papers presented at the 1994 Clinic on Library applications of Data Processing, April 10-12, 1994]
Bibliographic record
Abstract
Authors and readers in an age of electronic texts / Jay David Bolter -- Electronic texts in the humanities : a coming of age / Susan Hockey -- The Text Encoding Initiative : electronic text markup for research / C.M. Sperberg-McQueen -- Electronic texts and multimedia in the academic library : a view from the front line / Anita K. Lowry -- Humanizing information technology : cultural evolution and the institutionalization of electronic text processing / Mark Tyler Day -- Cohabiting with copyright on the nets / Mary Brandt Jensen -- The role of the scholarly publisher in an electronic environment / Lorrie LeJeune -- The feasibility of wide-area textual analysis systems in libraries : a practical analysis / John Price-Wilkin -- The scholar and his library in the computer age / James W. Marchand -- The challenges of electronic texts in the library : bibliographic control and access / Rebecca S. Guenther -- Durkheim???s imperative : the role of humanities faculty in the information technologies revolution / Robert Alun Jones -- The materiality of the book : another turn of the screw / Terry Belanger.
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.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.027 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".