Statistical positivism versus critical scientific realism. A comparison of two paradigms for motivation research: Part 1. A philosophical and empirical analysis of statistical positivism
Bibliographic record
Abstract
In this two-part publication, we compare two paradigms—statistical positivism and critical scientific realism—in their application to research on academic motivation. In the first part, the propositions of statistical positivism and their applications to psychological research are presented. An empirical study in this part combines self-determination and achievement goal theories and builds a statistically integrated model of motivation of 385 college students using path analysis. This part ends with a critical analysis of this statistical model and the knowledge about motivation that it provides. In the second part, the propositions of critical scientific realism are articulated. An empirical study in Part 2 utilizes these propositions and initiates realist interviewing of 12 purposefully selected students. Using within- and between-case analyses, a model of a motivational mechanism of successful university students is proposed. The authors conclude that the continued use of statistical positivism generates minimal new knowledge about the mechanisms of academic motivation. This paradigm should be replaced with the realist one and a case-based methodology, which have a better chance to advance research and improve understanding of academic motivation.
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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 | 0.090 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.003 | 0.048 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".