Knowledge theories can inform evaluation practice: What can a complexity lens add?
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
Abstract Programs and policies invariably contain new knowledge. Theories about knowledge utilization, diffusion, implementation, transfer, and knowledge translation theories illuminate some mechanisms of change processes. But more often than not, when it comes to understanding patterns about change processes, “the foreground” is privileged more than “the background.” The foreground is the knowledge or technology tied up with the product or program that prompted the evaluation. The background is the ongoing dynamics of the context into which the knowledge is inserted. Complex adaptive system thinking encourages greater attention to this context and the interactions and consequences that result from the intervention, making these the forefront of attention. For the evaluator, there are implications of this shift in thinking. Process evaluations should be designed to capture the fluidity of the change process. Impact and outcome evaluations will require long time frames. Complex adaptive system thinking also encourages multilevel measures, a focus on structures, and capacity to assess the possibility of whole system transformation (whole school, whole organization) as a result of the newly introduced program or policy. For the people involved in the innovation, there is a corresponding shift from a focus on their knowledge (and competence) to assessment of their learning (and system‐level capability). New ways to interpret fidelity in these situations should therefore be developed. © Wiley Periodicals, Inc., and the American Evaluation Association.
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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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