Bibliographic record
Abstract
As the body is not quite young enough to be “child” and not yet old enough to be “adult,” the social, political, cultural, discursive, material ideas and imaginings that constitute “youth” lie directly at the intersection of the scholarly concerns of queer studies and education. What is the imaginary of youth? Simultaneously, how are material realities of those called youth, particularly in relation to genders, sexualities and race, bound together with the imaginaries constructed about them? Drawing from a research project at a camp for queer and transgender youth and a course in sexuality studies in education, it is particularly productive to think about how knowledges are inhabited, raced and produced by imaginaries such as “good” youth, “good” adult, risk, and safety. How do questions such as “What will the youth grow (up?) to be?”; “What does success, failure, and learning look like?”; “Who stands up for whom?” reveal how “youth” as a category is employed in relation to gender, sex, race, ability and sexuality? Finally, this chapter explores how thinking about “youth,” and queer youth in particular, as both framed by categories and exceeding categorization offers rich space for queer studies and queer research in education. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.030 |
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".