Why Get Involved in Program Evaluations?: Toward a Model of Stakeholder Involvement Motives
Bibliographic record
Abstract
It is generally recognized that for any evaluation to be successful there needs to be a significant amount of involvement from program staff and other stakeholders who are invested in the program. Ideally, these individuals would be highly engaged in the evaluation process; however, in practice this ideal is rarely met. Two research studies were undertaken to explore different stakeholder motives for involvement in program evaluations and their relative importance in involvement decisions. In Study 1, stakeholder views of their own involvement (or lack thereof) in recent program evaluations were analyzed qualitatively using a thematic analysis approach. The findings revealed diverse stakeholder motives for evaluation involvement that include personal/human factors (e.g., opportunity for personal advancement), evaluation factors (e.g., clarity of evaluation goals) and organ-izational factors (e.g., funding implications). A second study looked more closely at these motives to determine their relative importance to evaluation involvement decision-making. Using the Q-sort method, three involvement profiles were identified which revealed different pathways to involvement based on individual concerns for program funding, learning opportunities, and evaluation quality. A preliminary model of stake-holder involvement motives is proposed in which the value and relevance of the evaluation are determined by expected outcomes of the evaluation.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".