Narrative goes to school, Boys themselves as educational research
Bibliographic record
Abstract
Narrative has entered the lexicon of educational research both as an end product and as a methodology. At the same time, stories of teachers, students, and schools have altered the horizon of popular literature and culture. "Teacher" has become one of the stock cultural archetypes that artists and politicians depend on for common platforms of remembrance and public discourse. As educational research embraces the social ambiguities and opportunities of narrative, discussions about how to audit and inflect such subjectivity have arisen. In particular, there are quandaries about acknowledging the dynamic boundaries of texts, as well as agreeing on norms of research competency. The purpose of this inquiry is to investigate narrative educational research as a process built upon researcher beliefs, needs, and motivations. Through an examination of 'Boys Themselves: A Return to Single-Sex Education ' (1996), this study focuses attention on the linguistic and rhetorical frameworks implicit in participant observation and the resulting research documents. 'Boys Themselves' tells the story of an esteemed independent boys' high school, and its headmaster's quest to alleviate a multitude of educational and societal ills. (Abstract shortened by UMI.)
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".