Bibliographic record
Abstract
There is currently a paucity of literature in the field of evaluation regarding the practice of reflection and reflexivity and a lack of available tools to guide this practice—yet using a reflexive model can enhance evaluation practice. This paper focuses on the methods and results of a reflexive inquiry that was conducted during a participatory evaluation of a project targeting homelessness and mental health issues. I employed an action plan composed of a conceptual model, critical questions, and intended activities. The field notes made throughout the reflexive inquiry were analyzed using thematic content analysis. Results clustered in categories of power and privilege, evaluation politics, the applicability of the action plan, and outcomes. In this case study, reflexivity increased my competence as an evaluation professional: The action plan helped maintain awareness of how my personal actions, thoughts, and personal values relate to broader evaluation values—and to identify incongruence. The results of the study uncovered hidden elements and heightened awareness of subtle dynamics requiring attention within the evaluation and created opportunities to challenge the influence of personal biases on the evaluation proceedings. This reflexive model allowed me to be a more responsive evaluator and can improve practice and professional development for other evaluators.
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 | 0.636 | 0.625 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.011 | 0.138 |
| Scholarly communication | 0.035 | 0.042 |
| Open science | 0.008 | 0.032 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".