Critical thinking: stress-testing competing reasons in the practical domain
Bibliographic record
Abstract
Abstract Harvey Siegel has argued that successful critical thinking requires both a critical mindset and expertise in evaluating reasons. In this article, I focus on the latter, the business of accurately appraising competing reasons in the practical domain. More specifically, I critically examine the limitations inherent in two commonly used epistemic frameworks: the balance account and reasons-for-and-against. I argue neither is suitable for scenarios requiring especially nuanced appraisals or complex metrics. This is because they primarily deal with somewhat crude approximations and/or binary choices. I will propose a new framework to address this concern. I dub this the Stress-Testing-Critique-Support-Explanationist Account (STCSEA). STCSEA is engineered to handle complex practical epistemic situations, ones where determining what to do is not straightforward or immediately calculable. To fully understand the accurate and reliable determination of competing reasons’ strengths and weaknesses, educators require sophisticated tools. Such tools can help us fully to grasp the concept and mechanics of rational defeat—that being figuring out the strength of competing reasons. To do so, we must also formulate and continuously refine an operationalized defeasibility taxonomy. I proceed as follows. I begin by providing a brief overview of the critical thinking debate. I then survey John Pollock’s work on defeaters. This work is germane to the conversation yet remains something of a lacuna in the contemporary philosophy of education literature. After this, I discuss the balance account and reasons-for-and-against, specifically their evident limitations. I then make a case for the superiority of STCSEA when it comes to handling ‘hard cases’. These are cases where what we should, must, or may do necessitates a complex computation. Such computations often exceed the mathematical or epistemic resources associated with ‘balancing metaphors’ and ‘for and against’ determinations.
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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 | 0.046 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| 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".