Conceptualizing indicator domains for evaluating 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
The focus of this paper is to share thinking about meta-level evaluation of action research (AR), and to introduce indicator domains for assessing and measuring inputs, outputs and outcomes. Meta-level and multi-site evaluation has been rare in AR beyond project implementation and participant satisfaction. The paper is the first of several associated with the Evaluative Study of Action Research (ESAR) in which we wish to establish the ways that espoused intents articulated in projects are realized and why certain approaches are adopted and seen to be effective. We seek to increase understanding of outcomes and impact of AR. Description is provided of multiple issues of complexity associated with establishing evaluative criteria and indicators categorized according to inputs, process, outputs, outcomes and impact. We explore theory associated with definition and practice of evaluation prior to presentation of the indicators. We think the time is ripe for deeper examination of AR practice and welcome the associated conversation and critique of our proposed indicators.
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 | 0.082 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it