Developmental Progress in Conducting Action Research
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
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.
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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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