Bibliographic record
Abstract
Lineages are genealogical sequences of genes, cells, organisms, or other biological entities. They populate the natural world and are discussed in various fields in biology. However, they barely receive philosophical scrutiny. In this dissertation, I explore philosophical issues regarding the nature of lineages, as well as their conceptualization and representation in science. First, I offer a historical overview of lineages in biology. I describe how biologists characterize lineages in evolutionary biology, developmental biology, paleontology, and other areas. This overview reveals the importance of lineages to theorization, experimentation, modeling, and other scientific practices. These diverse practices motivate the philosophical issues discussed in the following chapters. Second, I address the question of what is a lineage. Biologists and philosophers define them as genealogical sequences of biological entities (De Queiroz, 1999; Hull, 1980; Mishler, 2010). This broad definition reveals a belief that many of those scholars share: lineage is a single unified category in science or, in other words, a single type of entity in biology. I argue against this position and, instead, defend pluralism: the existence of a plurality of lineage types in biology. Third, I analyze the very concept of lineage. This concept is imprecise, and this imprecision may be harmful to scientific communication and reasoning. Similar concerns apply to the concepts of molecular gene and evolutionary novelty (Brigandt & Love, 2012; Kitcher, 1992; Waters, 2014). I compare the concept of lineage with these concepts. While all of them play beneficial roles in scientific integration, I argue that the concept of lineage facilitates a distinctive type of integration among scientists. Finally, I discuss how biologists represent lineages and why these representations matter to science. Biologists typically represent lineages in a simplistic, idealized way. Most philosophers consider these representations important to science only insofar as they result in improved theories and representations of nature (Potochnik, 2017; Velasco, 2012; Weisberg, 2013; Wimsatt, 2007). I argue that this view is limited. Representing lineages also results in collaboration among scientists and other social, non-representational activities that are central to science.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".