Identifying Paralogisms in Two Ethnically Different Contexts at University Level
Bibliographic record
Abstract
Although educational researchers have long tried to answer the question ‘Who reasons well?’, little has been done in regards to the influence of culture on argumentative reasoning quality. Among the factors that have been related with the construction of valid arguments, counterarguments and rebuttals by adults are: explicit argument training, task instructions and prior knowledge. No clear evidence exists regarding the influence of the ethnical background on the flaws or fallacies of reasoning. The present study applies the recent theory of paraschemes as a tool to identify university students’ paralogisms in a common argument-mapping task on everyday issues in two different cultural contexts: one European (Spain) and one Middle Eastern (United Arab Emirates). Our analysis showed that the influence of ethnical background was not statistically significant regarding the type and amount of paralogisms committed. On the contrary, the participants’ study major, being business or education, was shown to influence the production of argument fallacies. Implications of these findings for higher education are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".