Bibliographic record
Abstract
This paper provides an invitation to analytic abduction, an emerging approach to qualitative research . Like deduction and induction, abduction is a mode of inquiry. In a general sense, abduction forwards explanations for novel or surprising observations. In a more practical sense, abduction aims to combine the strengths of both inductive and deductive inquiry by reasoning from concrete data (similar to induction), but using this data to extend, refine, or refute existing theories or propositions (similar to deduction). In this paper, we provide an overview of how and why abduction was developed for qualitative research before demonstrating how to apply analytic abduction to real-world data. Our examples connect data to longstanding and well-researched theories in psychology to highlight the utility of abduction for psychological researchers. We argue that analytic abduction is an ideal resource for qualitative psychologists, as the approach emphasizes qualitative data while leveraging such data to shape theory. This focus on theory provides ample opportunities to use qualitative work to inform concepts central to psychological science, including those that are primarily tied to experimental design, quantitative methods, and deductive reasoning .
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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 | 0.189 | 0.409 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.054 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.025 | 0.053 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".