Zuber-Skerritt, O., Fletcher, M., & Kearney, J. (Eds.). (2015). Professional learning in higher education and communities: Towards a new vision for action research. London, UK: Palgrave-Macmillian.
Bibliographic record
Abstract
Zuber-Skerritt, Fletcher, and Kearney’s nine chapter volume introduces their vision of action research which they call participatory action learning and action research (PALAR). They describe PALAR as “holistic, inclusive, and participative to enable transformative improvement in human living conditions (p. 2)”. The creation of this methodology provided the authors with an opportunity to define their epistemological and ontological stances. They state that they subscribe to the critical realist perspectives and prioritize research which is humanitarian and which is empowering. Their methods are informed by a multitude of theoretical perspectives such as hope theory, complexity theory, experiential learning, negative dialects, living theory, grounded theory, critical theory, and phenomenology. In Chapter Two professional learning is described as a body-brain-mind dynamic which requires attention and reflection. Critical reflection as it relates to action research is discussed in Chapter Three. The authors propose a multilevel reflective process. In the last chapter of this section the authors address action research itself and introduce the “action research family” which includes different forms of action research. It is here that PALAR is thoroughly explained as a unique form of action research. Its philosophy, methodology, theory, process, and model are outlined.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".