Bibliographic record
Abstract
This paper raises three enduring areas of debate around inner-city education: 1) the extent to which schools are the best places to intervene to improve outcomes for poor children – the policy question is whether that is enough to expect given the very substantial resources devoted to schools, or whether a larger share of overall resources would be better used to support initiatives around early childhood, employment, housing, or better income support programs; 2) the best strategies for urban schools to improve student outcomes – many school strategies have been about supplementary programs for high need communities but more recently focus has shifted to improving teaching and learning practices in high poverty schools; 3) the challenges of building and sustaining political support in addressing urban education issues – not only are the politics of urban areas highly fractious, but it is difficult to create sufficient support in the larger polity to sustain improvements in urban education. Contemporary approaches to poverty and education replicate many ideas and initiatives that were actually in place decades ago, raising the question of how to use and learn from the experience of the last several decades so that the same choices and mistakes are not repeated.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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".