Places of possibility: youth research as creative liberatory praxis
Bibliographic record
Abstract
This work builds upon and extends critical youth studies scholarship concerned with strategies for engaging with young people as active change agents with arts-based research to advance theory and practice for democratic and emancipatory research with young people. In this paper, we share a collaborative self-study where we critically reflected upon our distinct experiences doing arts-based participatory youth research. We applied a critical analysis to our pedagogical and research practices in a systematic manner to garner insights, strategies, and tools that can enhance and improve them. We conceive of ‘places of possibility’ as literal and metaphorical spaces where young people are afforded the tools and resources necessary to imagine alternative realities, identities, and systems than what currently exists, primarily through art and creative practices (Goessling, K. P. 2017. “Youth Learning to be Activists: Constructing ‘Places of Possibility’ Together.” Critical Questions in Education 8 (4): 418–437. doi:17/10/goesslingfinal.pdf). We examined our art and creative practices for generating pedagogical places of possibility at three intersections: (1) creative practices as pedagogy, (2) moments of encounter, and (3) thick participation. Our findings provide strategies and tools for designing transformative research spaces with young people that are humanizing and liberatory places of learning and knowledge production.
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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 | 0.054 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.106 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.006 |
| 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".