Bibliographic record
Abstract
It is unclear whether the first law forbids any conceivable scenarios or whether it is a tautology. This paper examines the first law with the goal of clarifying which scenarios it allows and which ones it forbids. I begin by highlighting a number of problems with the current formulations of the first laws for theories, methods, and questions, as well as the respective rejection theorems. New formulations for these laws and theorems are suggested to ensure their uniformity and the validity of their deductions. Next, I discuss a series of scenarios of theory replacement allowed by the first laws, such as the replacement by negation, the replacement by an answer to a different question, the replacement that involves the rejection of the question, and the replacement by a higher-order proposition. I then consider scenarios that are forbidden by the first law and show that this class only includes cases of rejection without replacement such as instances of element decay. This creates a dilemma. On the one hand, if cases of rejection without replacement are classified as non-scientonomic phenomena, the first law is a tautology. On the other hand, if such cases are classified as scientonomic phenomena, then the first law is not a tautology, but these cases stand as violations of the first law. The paper resolves this dilemma by opting for the former option: cases of rejection without replacement such as element decay due to catastrophic loss of records or destroyed communities are non-scientonomic, and should be considered as outside the scope of our discipline.
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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".