Bibliographic record
Abstract
Correlations and Causal Reasoning Near the end of Chapter 8, Case 8E involved the drawing of a generalization from a study that tried to establish a correlation between two things – having a busy social life and avoiding colds. Implicit in the conclusion is the causal claim that the first thing, the busy social life, caused or was a causal factor in the occurrence of the second thing, avoiding colds. The Argument from Correlation to Cause can be a reasonable argumentation scheme if it meets the correct conditions, but when these are not met, fallacious reasoning occurs. In this chapter we will concentrate upon three types of causal reasoning that can prove problematic: (1) that which involves the concluding of a causal relation from a correlation or a mere temporal sequence, (2) reasoning that confuses the causal elements involved, and (3) that which predicts a negative causal outcome for a proposal or action, perhaps on the basis of an expected causal chain. The labels we will use for these three are post hoc reasoning, Misidentified Cause, and Slippery Slope reasoning. Understanding causal reasoning and determining when it is fallacious are made difficult by the lack of any clear agreement on how to analyze the concept of causation. We will be able to detect cases in which something is clearly wrong with a causal argument, but more contentious cases will be a different matter.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".