Bibliographic record
Abstract
This qualitative case study investigated the impact of Workforce Investment Act (WIA) funding on the providers and planners of programs for incumbent workers in one Midwest WIA region. It examines the collaboration and power conflicts that are part of planning and implementing this legislation for the stakeholders. The study applied Matland's ambiguity/conflict framework to WIA implementation. The analysis revealed four themes that are important to policy makers and planners alike. The themes, change agent conflict, power broker conflict, policy interpretation conflict, and ambiguity of means, address the impact of the WIA and related processes on programs for incumbent workers. Conflicts over participants' roles, the interpretation of the legislation, and ambiguity about the process of implementation emerged. This article suggests methods for stakeholders to collaborate and address the needs of incumbent worker development.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.039 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".