Extending the Responsibilities for Schools beyond the School Door
Bibliographic record
Abstract
In this article, the authors use qualitative and quantitative research to identify the performance of social actors whose decisions impact students and teachers, elucidate a set of measurements for their performance, and offer both a theoretical and research justification for these measurements. The work challenges two faulty assumptions behind the No Child Left Behind (NCLB) legislation that make it more likely that school curricula will continue to be unrepresentative of diverse experiences and that far too many children will continue to attend schools under unnecessarily trying conditions. The first faulty assumption is the legislation's location of school ‘problems’ or the ‘problems with schools’ as beginning and ending at the school door. A second assumption that the authors’ development of research-based rubrics seeks to challenge is a prevalent attitude in US society regarding individual responsibility for personal success or failure, which supports the thrust of NCLB in the public imagination. Like the ill-distribution of economic possibilities despite people's hard work, rubrics holding the various public stakeholders in education are required to appropriately expand responsibility for student success beyond schools and teachers.
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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.024 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".