Developmental Progress in Conducting Action Research
Bibliographic record
Abstract
Action research is widely acknowledged as an effective framework of empowerment and emancipation to improve a social situation or condition—an intent that appeals to leaders wishing to create improvement, particularly in low socioeconomic and disadvantaged communities. Validity of such espousals has been substantially unexplored, and where evaluations have occurred, they have been focused more on process than impact. A group of international researchers were engaged in an evaluative study of more than 100 action research initiatives, using a variety of methods, tools and conceptual frameworks. The maturity model for action research is one of the conceptual frameworks adopted in this Evaluative Study of Action Research. Maturity models have their origins in the capability maturity model developed to address the poor performance of software projects delivered to the US Department of Defence in the 1980s. The purpose of the capability maturity model was to help contractors increase capability to improve their software engineering processes from an ad hoc state to a more formal and repeatable state and, eventually, to optimize the processes to deliver consistent outcomes. Maturity models have now found their way into many other organizational contexts, such as project management, knowledge management, process management and research capability. However, the term ‘maturity model’ is usually associated with business jargon and quantitative research. Therefore, the authors of this article felt the concept could be made more palatable to action researchers by rephrasing it as ‘maturity profile’ to improve the ways in which they manage their projects to deliver sustainable outcomes. This resulted in the development of the maturity profile described in this article. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.389 | 0.370 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.030 | 0.035 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.019 | 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".