Embedded, Participatory Research: Creating a Grounded Theory with Teenagers
Bibliographic record
Abstract
Objective – This project, based on a study of the impact of art programs in public libraries on the teenaged participants, sought to show how library practitioners can perform embedded, participatory research by adding participants to their research team. Embedded participatory techniques, when paired with grounded theory methods, build testable theories from the ground up, based on the real experiences of those involved, including the librarian. This method offers practical solutions for other librarians while furthering a theoretical research agenda. Methods – This example of embedded, participatory techniques used grounded theory methods based on the experiences of teens who participated in art programs at a public library. Fourteen teens participated in interviews, and six of them assisted in coding, analyzing, and abstracting the data, and validating the resulting theory. Results – Employing the teenagers within the research team resulted in a teen-validated theory. The embedded techniques of the practitioner-researcher resulted in a theory that can be applied to practice. Conclusions – This research framework develops the body of literature based on real-world contexts and supports hands-on practitioners. It also provides evidence-based theory for funding agencies and assessment. In addition, practitioner-based research that incorporates teens as research partners activates teens’ voices. It gives them a venue to speak for themselves with support from an interested and often advocacy-minded adult.
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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 | 0.122 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".