Bibliographic record
Abstract
This paper opens with a family anecdote in which my future mother in-law, when asked what wise advice she would offer undergraduate university students, replied, “I would tell them it’s not going to be okay.” Can we learn to keep engaging with the world despite its inevitable disappointments? I propose that Stoic philosophy, by “orienting” our “attention” and “courage,” can help us navigate the troubled post-COVID world we share. To help make this more concrete, I describe a critical moment I observed in which a maskless shopper insulted fellow patrons in a grocery store for wearing a mask. I then develop the Stoic themes of acknowledgement (a commitment to the facts) and affinity (reaching out to others to build community). In the conclusion I return to the “maskless shopper” incident to consider how my two Stoic themes might help open a dialogue with this person. After discussing the limitations to such an undertaking, given the surge in populism over the last decade, I conclude with the appropriately tough-minded Stoic proposition that despite the obstacles, we must keep trying.
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 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.060 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".