Commentaries on “Abductive Theory Construction”
Bibliographic record
Abstract
Abstract This paper assembles five comments on Janiszewski and van Osselaer's (this issue) article that promotes abductive research as a way to generate new psychological theory. The review process began by asking those making comments to be part of collaborative communication between themselves and Janiszewski and van Osselaer. The five comments arising from that process provide well‐honed insights into the strengths and weaknesses of the abductive research. The first commentary, by Frank Kardes, offers convincing evidence showing that the techniques of abductive thinking are similar to other explorative techniques currently being successfully used in deductive research. Eileen Fischer sees abductive thinking as integral to inductive and qualitative thinking as it facilitates the generation of new constructs and remaps established ones. Stephen Spiller explores the implication of starting from interesting and paradoxical data rather than from established theory. The research challenge then requires a focus on strategic sampling of methods, responses, and critical constructs that confirm or limit a provisional theory. Aparna Labroo articulates the benefits of abductive thinking to help resolve complex practical problems, but warns against the proliferation of multiple findings that may be difficult to validate. Finally, Bublitz and Peracchio celebrate the value of abductive research to help resolve social issues and enable the fruitful merger of publishable research with personal social action.
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.
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 | 0.096 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.019 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".