Reconstructing Philosophical Genealogy from the Ground Up: What Truly Is Philosophical Genealogy and What Purpose Does It Serve?
Bibliographic record
Abstract
What is philosophical genealogy? What is its purpose? How does genealogy achieve this purpose? These are the three essential questions to ask when thinking about philosophical genealogy. Although there has been an upswell of articles in the secondary literature exploring these questions in the last decade or two, the answers provided are unsatisfactory. Why do replies to these questions leave scholars wanting? Why is the question, “What is philosophical genealogy?” still being asked? There are two broad reasons, I think. First, on the substantive side, the problem is that genealogical models will get certain features of the method right but ignore others. The models proffered to answer the first question are too restrictive. The second reason is that the three essential questions to ask regarding the nature of genealogy are run together when they should be treated separately. In the following paper, I address these problems by attempting to reconstruct genealogy from the ground up. I provide what I hope is an ecumenical position on genealogy that will accommodate a wide variety of genealogical thinkers, from Hobbes to Nietzsche, rather than a select few. Therefore, I examine two of the three questions above: What is philosophical genealogy and its purpose? I argue there are seven main features of genealogy and that these features may be used as a yardstick to compare how one genealogist stacks up to another along the seven aspects I outline in the paper.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.011 | 0.031 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".