Bibliographic record
Abstract
Purpose Much has been written about the crisis in the Humanities even as student interest in the Humanities continues to decline. In the so-called “post-truth,” “post-COVID19” period,” however, the Humanities deserve attention for the important role they must play in preparing students for the world during a period of dramatic change. Design/methodology/approach Discussion focuses on the “post-truth” period and how the Humanities have a role in confronting misinformation and “fake news.” It provides specific actions for how those in the Humanities might address the current situation. It relies on the author’s considerable background as a university Dean and President over a period of over 40 years and draws on a variety of written material addressing the future of the Humanities. Findings In a period when the world confronts unprecedented change, when misinformation is confused with the truth and when social media exercises so much influence, students more than ever need the insight and context of the Humanities to mitigate the cant, bogus claims and questionable ethics that so much shape the world. Responsibility falls to faculty as they must make clear to their students how the Humanities provide a perspective that allows students to work through the big questions of their time. Research limitations/implications Much has been written about the challenges facing the Humanities. It is hoped that this paper will generate additional discussion on how the Humanities might assert themselves during what are troubling times in higher education. Originality/value The author’s long experience as a senior university administrator provides a perspective that faculty and administrators might find useful as they consider the future of the Humanities at their institutions.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".