Bibliographic record
Abstract
Although natural history as a genre had classical, medieval, and Renaissance forms, seventeenth-century natural histories played a distinctive role in relation to projects – especially large-scale projects of expropriation, economic exploitation, planned mobility, and settlement. Sites targeted for such projects must be shown to be thoroughly known for the projects proposed to seem feasible or profitable and the risks involved calculable and worthwhile; at the same time, portraying these sites as vacant, waste, or unimproved – tabula rasa, white paper, terra nullius – supplied an important justification and argument for the kinds of intervention and expropriation these projects required. In this context, natural history (conceived of as embracing both works of nature and the achievements of art) became part of a larger epistemic project that predicated the assessment of a situation’s future potential on the knowledge of its present state and resources – often known through local testimony – while simultaneously downplaying past interventions, including even earlier natural histories. Such histories partook of the nature of projects while serving as important instruments for projectors. Though visible in natural histories of various parts of the early modern world, this dynamic is particularly clear in the case of natural histories in Cromwellian and later Stuart Ireland.
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 | 0.001 | 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 | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".