Bibliographic record
Abstract
1. INTRODUCTION: BIOPROSPECTING, ETHNOPROSPECTING, AND THE SCIENCE OF SINGULAR THINGSThe belief prevails in some scholarly circles that Gottfried Wilhelm Leibniz undertook his official duties as historian of House of Brunswick only grudgingly, preferring instead to devote his energy to his true love: apriori reflection upon universal truths.' In fact, however, Leibniz pursued his historical research with great interest, if more slowly than his employer would have liked, and even saw it as inseparable from his broader intellectual project - his philosophy, if you will. The perception that Leibniz was better suited to a priori speculation than to investigation of particulars is one that hounded Leibniz even during his own lifetime. Thus he complains to Swedish Slavist Johan Gabriel Sparwenfeld in a letter of 1698 that people criticize me when I attempt to take leave of study of mathematics, and they tell me that I am wrong to abandon solid and eternal truths in order to study changing and perishable things that are found in history and its laws.2In a 1708 draft of a proposal to Peter Great for classificatory system to be used in eventual library of St Petersburg Academy of Sciences, Leibniz identifies history, alongside mathematics and physics, as one of three 'Realien' or distinct domains of science, namely, one that involves explanation of times and places, and thus of singular things [res singulares], including the descriptions and attainments of kingdoms, states, and peoples.3 Besides what might be called civil or political history, another central branch of history - again, understood as science of singular things - was study of plants, animals, and minerals. This study was generally carried out with an eye to utility of things studied. Now it is uncontroversial to note that a central motivation for colonial expansion of Europe throughout world in early modern period was prospect of natural resource extraction, and this practical end required for its success a deepened theoretical grasp of real variety of things in nature. At same time as field of natural knowledge was expanding, there was a growing awareness of inadequacy of ancient texts, such as those of Dioscorides, Theophrastus, and Pliny, which for centuries had been able to pass as exhaustive classifications of kinds ofthing in natural world. What resulted was, to use Londa Schiebinger's term, early modern global system of 'bioprospecting': communal effort to discover and classify plants and animals (though mostly plants) throughout world (mostly those parts of world left unstudied by ancients), particularly with an eye to discovering useful kinds, and bringing them back to Europe for commercial gain.4 While Schiebinger's work has been very useful in drawing attention to a widespread early-modern phenomenon, in a number of respects what she describes fails to adequately capture full significance of prospecting activity of a figure such as Leibniz. By describing this activity here, we hope to add some nuance to scholarly discussion of of early modern bioprospecting.Some scholars have emphasized important role of cameralism, in German and Northern European context, for stimulating much natural-historical prospecting.5 One thing that has become clear from these studies in particular is that there is a sharp difference between German and Northern European context of prospecting on one hand, and other European (and particular Iberian) contexts on other. Much of this difference had to do with fiscal organization of smaller German principalities, duchies, and electorates, which often relied on inventive men such as Leibniz to find new streams of revenue from limited natural resources of sovereign's regio, very much in contrast with massive global enterprises of Iberians. …
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".