Bibliographic record
Abstract
A positive emphasis on political and cultural borders would seem to be, these days, rather intellectually unfashionable. Everywhere such borders are being placed in question as arbitrary, fictional, and pernicious, as serving to foster oppressively exclusivist modes of identity, as erecting boundaries between “us” and “them” that stand in the way of achieving mutual understanding and global justice. There are, to be sure, good reasons for this suspicion of borders, and much of value in the multiple forms of critique aimed at dismantling them: the political cosmopolitanism that thinks beyond the nation-state, seeking a decent life for all individuals; deconstructive accounts rightly drawing attention to the relational, dynamic, and overlapping character of group identities; universalist discourses emphasizing our common humanity, against xenophobia, racism and cultural bigotry. An aggressive insistence on borders of the sort that wants to keep out and keep in, or that limits moral concern to those who are supposedly my own as opposed to those who are not, does not serve the interests of justice, and is well worth criticizing. In this light, while one may applaud Herder's attempts to counter racism, imperialism, and ethnocentrism, his focus on the distinctness of peoples and cultures might well be perceived as false and dangerous, especially given the terribly violent history of ethnic nationalism in the intervening years.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.181 | 0.062 |
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".