Bibliographic record
Abstract
While emergentism is a frequently debated and contentious topic in some areas of philosophy, it is not discussed as often in the sciences. Where it does appear in scientific literature, it is usually a weak formulation that admits as emergent many entities and properties that would not be considered emergent under a stronger formulation. Premature admission of this sort sometimes occurs in the context of physics, but it is more likely to occur in higher-level sciences like biology. In this thesis, I examine the claim that life, a fundamental biological feature, is emergent. In order to do this, I begin by examining what counts as life. I settle on three features that are necessary for life as we know it, and I show that the smallest unit of life is the prokaryotic cell. I then examine the received view of emergentism and identify its key tenets. I consider two of these in depth, as they play a crucial role in my argument. Finally, I consider weaker formulations of emergence found in scientific literature in order to contrast them with the robust philosophical notion developed within this thesis. I argue that, based on a strong formulation of emergence, life should not be considered emergent, though some may dispute this position if a weaker version of emergence is adopted.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".