The Humanities in Public: A Computational Analysis of US National and Campus Newspapers
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
Academic defenses of the humanities often make two assumptions: first, that the overwhelming public perception of the humanities is one of crisis, and second, that our understanding of what the humanities mean is best traced through a lineage of famous reference points, from Matthew Arnold to the Harvard Redbook. We challenge these assumptions by reconsidering the humanities from the perspective of a corpus of over 147,000 relatively recent national and campus newspaper articles. Building from the work of the WhatEvery1Says project (WE1S), we employ computational methods to analyze how the humanities resonate in the daily language of communities, campuses, and cities across the US. We compare humanities discourse to science discourse, exploring the distinct ways that each type of discourse communicates research, situates itself institutionally, and discusses its value. Doing so shifts our understanding of both terms in the phrase “public humanities.” We turn from the sweeping and singular conception of “the public” often invoked by calls for a more public humanities to the multiple overlapping publics instantiated through the journalistic discourse we examine. And “the humanities” becomes not only the concept named by articles explicitly “about” the humanities, but also the accreted meaning of wide-ranging mentions of the term in building names, job titles, and announcements. We argue that such seemingly inconsequential uses of the term index diffuse yet vital connections between individuals, communities, and institutions including, but not limited to, colleges and universities. Ultimately, we aim to show that a robust understanding of how humanities discourse already interacts with and conceives of the publics it addresses should play a crucial role in informing ongoing and future public humanities efforts.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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