Apathy And The Modern Self: The Afflictions Of Modernity And Orientation Toward The Good
Bibliographic record
Abstract
Research to date on apathy has been limited to the technical spheres of politics, pedagogy, mass media, and business. Contrary to apathy’s characterization in recent scholarship, this work claims that apathy cannot be understood in terms of a decline in political engagement alone. Through a history of the idea of apathy beginning with the Stoic concept of apatheia, this work locates apathy in the epochal shift in epistemology and subjectivity which occurred between antiquity and modernity, and claims that apathy is a philosophical (rather than political) problem. Guiding research questions include: what allowed for the possibility of modern apathy, and what means might we have at our disposal to address apathy? Rather than treating symptoms, I argue that any response to apathy must engage with its epochal grounding conditions, and so rather than suggesting policy reforms or new legislation, I assess problems accompanying modern subjectivity and epistemology, and the place of the Good under modernity. This project also participates in the longstanding debate concerning the possibility of uniting sense and reason, a problem known in antiquity and addressed by communication theorists and Romantic poets. I argue that the commingling of sense and reason is another way of describing openness to an encounter with the Good, and under modernity such commingling might result from aesthetic exercises. I consider McLuhan and Foucault thinkers whose work can be read as a form of áskēsis that extends the ancient philosophical tradition into modernity in order to encourage spiritual work in the present. Through readings of McLuhan and Foucault’s engagement with antiquity, I then suggest that aesthetic exercises arising out of the modern milieu may offer a response to apathy and its grounding epistemological and subjective conditions. This work attempts to broaden the contemporary understanding of apathy, and to reconnect the discourse on apathy to its grounding conditions – subjective and epistemological sunderings which have been intensified and normalized under modernity. This broadening and reconnection demand that apathy is understood in a more complete way, not simply in terms of its immediate consequences for the technological society.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".