Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it