Data Analyses using the Action Project Method Coding Technique: A Guide
Bibliographic record
Abstract
The qualitative action-project method (A-PM) was developed in counseling psychology and is useful for studying human actions in various contexts. With this article we provide a guide to A-PM data analysis with a focus on the method’s coding technique. We briefly outline the theory underpinning the method as well as the different phases of data collection. The A-PM data analysis happens in parallel from a bottom-up and top-down approach, where researchers consider the data closely for what participants are doing, how they are doing it and the ways in which their actions are directed by their overall goals. We add to the existing literature by detailing the coding technique, providing examples at each stage of analysis, as well as reflect on the possibilities for adapting the protocol for different types of research. Our aim is to support researchers in their efforts to undertake the method.
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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 | 0.138 | 0.143 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".