The Humanities in Public: A Computational Analysis of US National and Campus Newspapers
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.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".