The Humanities Matter! Infographic
Bibliographic record
Abstract
<p>The University College London (UCL) Centre for Digital Humanities–in collaboration with 4Humanities– created this The Humanities Matter! infographic with statistics and arguments for the humanities in high-impact visual form.</p>\n<p>The Humanities Matter! was created by the UCL Centre for Digital Humanities under the supervision of its director Melissa Terras, who is also a co-leader of 4Humanities. Terras collected data for the infographic with assistance from Ernesto Priego (an international correspondent for 4Humanities), Lindsay Thomas (lead research assistant for 4Humanities), Victoria Smith (research assistant, Humanities Computing, U. Alberta), and the other co-leaders of 4Humanities: Christine Henseler, Alan Liu, Geoffrey Rockwell, and Stéfan Sinclair.</p>\n<p>Please download The Humanities Matter! and help 4Humanities circulate it ; feel free to print in small or large format and distribute. The Humanities Matter! is licensed under a Creative Commons Attribution 3.0 Unported License.</p>\n<p> </p>\n<p> </p>
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 | 0.000 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".