Bureaucratic Competence as an Essential Factor in Cross-Cultural/Multicultural Program Evaluations
Bibliographic record
Abstract
Abstract: While we may all agree in principle that both implementers and evaluators should be culturally sensitive and ethical as well as instrumentally effective in their work practices, we often ignore the extent to which these practice goals may conflict with one another in achieving bureaucratic competence, particularly in a multicultural society. Reconciling them requires us to acknowledge the indispensable role of responsible program evaluation in this effort, one that addresses: both the employees and the recipients of programs; the need for evaluators to be open to both theoretical and operational contributions to the field; the signal role of bureaucratic, as well as electoral, modes of representation; the indispensable function of affirmative action and pay equity programs in reconciling diversity and fairness; and the principle that subjects in evaluation and implementation processes should play a more significant role than the passive status assigned them by traditional bureaucracy and applied social science.
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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 | 0.312 | 0.426 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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; 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".