Bibliographic record
Abstract
As qualitative researchers, we have been increasingly attracted to incorporating techniques that increase the involvement of former research subjects in research projects. This attraction springs from a deepening understanding of the importance of the human relationships we create with those we study. Such increased inclusivity, however, has not proven to be easy. My experiences as director of a large collaborative ethnographic evaluation project are examined in light of the conflicting pulls I experienced between the desire to create more inclusive research and the simultaneous limits on the possibilities for certain kinds of players to be fully involved. The roles of university researcher, program administrator, teacher, and learner in two adult education programs are examined in terms of the possibilities for and limitations on inclusivity.
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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 | 0.687 | 0.634 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.035 | 0.112 |
| Scholarly communication | 0.053 | 0.062 |
| Open science | 0.015 | 0.068 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".