Imperial (Re)assemblages and Reconstructions: Intimate Terrors and Ontological Possibilities
Bibliographic record
Abstract
The jade eucalyptus trees stand in the centre of the village of Aphania, holding secrets, gently exuding their soft scent. The aroma strikes deep chords within me, invoking images of my many walks with many important persons in my life (friends, father, mother, grandfathers, grandmas, aunts and uncles, cousins). This small village was constituted by many different struggles, joys, the sweat of so many ancestors that brought it to life again and again even when it was supposed to disappear. I am remembering as I walk today in the midst of the eucalyptus scents, this village of approximately a 1000, full of life, its cobbled streets walked daily by all peoples, Muslims, Christians, Greek, Turkish, maronites, rich, poor, roma peoples, black, olive-skinned peoples, us. It is in this village that the imperial-sovereign machine ground some of us, and our land, into its surplus, that vital energy that would make possible its most anxious desire, the creation of buffers: ethno-national conflicts. The village next door kicked out much of its population, those Turkish Cypriots who were deemed contaminants to a purist ethno-nationalist project of Greekness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.063 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".