Bibliographic record
Abstract
In this article, I lament, problematize and illustrate the inviolability of the culture wars spectrum(s) as a modernist/ubermodern matrix in which we’re trapped in ideological groupthink and exclusion of the other. Intrinsic to the binary language constructs of left-right, liberal-conservative, etc. is an us-them assumption of polarization that is impotent in creating a justice society or achieving a greater good. The spectrum is ideological, depending on demonizing the other, in the name of opposing notions of ‘freedom’– a euphemism for autonomous self-will. The self-will and othering of any us-them cannot be resolved by changing sides without changing spirits. ‘Calling out’ is easy; ‘calling in’ much harder. Having grieved and critiqued spectrum ideology, I propose an alternative vision adapted from George P. Grant (Canadian political philosopher) and Simon Weil (French philosopher/activist/mystic). To do so, I appropriate Grant's interpretation of the Socrates’ ‘Good’ fulfilled in Christ's such that Love/the Good are given primacy rather than freedom as self-will. That vision attempts to transcend spectrum groupthink and strives for an alternative society that invites divergent streams beyond their partisan zeal to draw near for a conversation concerning the greater goods of restorative justice and inclusive care.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.106 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| 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".