Bibliographic record
Abstract
Neomedievalisms are cultural practices that breathe a bouquet of premoderns as permanent rehearsals of coming events. Where medievalists may be prone to police the post-medieval weald for inauthentic medievalisms, neomedievalists embrace the articulation and mobilisation of metahistorical anachronisms. To the medievalist, medievalisms provide powerful indexes that reveal how post-medieval societies have variously imagined ‘little middle ages’ to suit modern agendas. To the neomedievalist, medievalisms are theory-fictions that facilitate ludic speculation on non-modern futurities. While neomedievalist theories have emerged in a variety of fields since the early 1970s — notably in cultural studies of medievalisms, international relations and literary theory — there are few applications that synthesise and put the methodologies of these diverse fields into practice. thN Lng folk 2go applies this extant scholarship as an extradisciplinary practice, dramatising the neomedieval turn in (quasi)objects, persons, work, education, travel, food, ethnicity, media, art, hypereconomics and technology. This speculative journey is ghost authored by a trinity of neomedievalist narrators — Journeyman, Anchorite and Host — each relic-ing their own curious neomedieval futurities
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.017 |
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".