Bibliographic record
Abstract
Background: Excessive evaluation anxiety (XEA) refers to disproportionate or increased evaluation anxiety among those affected by evaluation (e.g., stakeholders) characterized by the sole presence of negative consequences. It can compromise evaluator-stakeholder relationships, presenting as a barrier for program evaluation. Moreover, XEA can both cause and be caused by resistance to evaluation, which is an interrelated topic that shares many common causes, characteristics, and mitigation strategies. The participatory and interactive nature of modern evaluation approaches can exacerbate the presence of XEA. However, researchers have not explored the current state of literature on XEA. Purpose: To explore the current state of the literature on XEA over the past 20 years. Setting: Not applicable. Intervention: Not applicable. Research Design: Literature review. Data Collection and Analysis: We conducted a literature search of Academic Search Complete, Web of Science, and Scopus. We complemented the database search by a journal search of the American Journal of Evaluation, Evaluation, and the Canadian Journal of Program Evaluation. We then conducted a thematic analysis of the articles that met the inclusion criteria. Findings: Upon review of the articles, we identified four main themes in the literature related to XEA. Specifically, XEA: leads to poor evaluator-stakeholder relationships; is influenced by cultural factors; can be mitigated through the development of interpersonal skills; and can be mitigated through a systematic and evidence-based approach to 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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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 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".