Participatory action research and evaluation: creating change on the Yukon flats
Bibliographic record
Abstract
This inquiry addresses the applicability of the Participatory Action Research (PAR) approach in executing Tribal, village-based evaluations of the United States Environmental Protection Agency's (US EPA)Indian General Assistance Program (IGAP). The research question follows: can the PAR approach serve as an effective method for performing Tribal evaluations of federal programs in Native America, particularly the EPA IGAP? The sub-questions go further to address the specific areas in which the PAR approach's effectiveness is being examined: 1. building local administrative capacity in project management, specifically the evaluation process. 2. raising critical consciousness at the local level concerning the programmatic life cycle. 3. empowering the local community through the participatory process. and 4. communicating Tribal perspectives to federal agencies on their programs. The research environment consisted of primarily two native Athabascan villages in the rural, northern interior of Alaska. The methodology employed included: village-based community meeting evaluation workshops similar to semi-structured focus groups, one-to-one capacity-building in project evaluation, informal conversations, and one-to-one semi-structured interviewing. The PAR approach serves as the basis for my research methodology. This approach addresses the integrative nature of the inquiry, as it examines the process and its outlining effects on the local participants while also creating a data set on the Tribal perspective of EPA IGAP. The intended outcome of the project is to have examined a process which can provide results on a variety of levels, to have created a place for learning for myself, for local participants, and for federal agency staff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".