Toward Sustainable Growth: Lessons Learned Through the Victorian Women Writers Project
Bibliographic record
Abstract
<p class="p1">This case study offers strategies for TEI-based projects with limited funding. By focusing on the needs of our volunteers, the Victorian Women Writers Project has developed truly collaborative relationships with the project’s partners. Contributions to the project’s resources have grown out of digital humanities survey courses, literature classes, and independent work. The paper concludes with a brief sketch of our efforts to support continued work by rethinking our social media outreach and our online presence. <hr /> <p class="p1">Cette étude de cas offre des stratégies pour les projets TEI (initiative pour l’encodage de texte) dont le financement est limité. En mettant l’accent sur les besoins de nos bénévoles, le projet Victorian Women Writers a mis au point des relations véritablement collaboratives avec les partenaires du projet. Les contributions aux ressources du projet sont issues des cours d’introduction et des classes de littérature en humanités numériques, et de travail indépendant. L’article conclut par un bref résumé de nos initiatives afin d’appuyer le travail continu en réévaluant notre diffusion dans les médias sociaux et notre présence en ligne. <p class="p1"> <p class="p1"><strong>Mots-clés: </strong>Encodage TEI; HN féministes; durabilité
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 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 | 0.001 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".