Knowledge theories can inform evaluation practice: What can a complexity lens add?
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.
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 | 0.352 | 0.499 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.031 | 0.013 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.047 | 0.084 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".