Beyond Rationalization: Inverting the Pyramid, Remaking the Educational Sector
Bibliographic record
Abstract
Over and over again across the 20th century and a decade into the 21st, Americans have sought to rationalize their schools, with limited results. Is there a better way? In the pages that follow, I argue that there is. At base, you could say that the entire American educational sector was put together backwards. Beginning early in the 20th century, teaching became institutionalized as a highly feminized, low-status field; universities, unwilling to associate with training low-status teachers, trained instead a set of male administrators to control and direct those teachers; failures of schools prompted additional levels of control and regulation from afar, further diminishing autonomy and making the field less attractive to talented people. Successful systems from abroad essentially do the reverse. They choose their teachers from among their most talented students; they train them extensively; they provide opportunities for them to collaborate within and across schools to improve their practice, they provide the needed external supports for them to do this work well; and they support this educational work within stronger welfare states. This is true of East Asian countries like Korea and Japan, but it is also true of non-Confucian countries like Canada and Finland. While it is not yet clear how much of this success are due to which of these factors, it is clear that many of the world’s leading countries take a fundamentally different approach than the one favored in the United States. As a recent Organization for Economic Cooperation and Development (OECD) volume sums up what it sees as the lessons from nations that lead the Programme for International Student Assessment (PISA) rankings: “The education development progression is characterized by a movement from relatively low teacher quality to relatively high teacher quality; from a focus on low-level basic skills to a focus on high-level skills and creativity; from Tayloristic forms of work organization to professional forms of work organization; from primary accountability to superiors to primary accountability to one’s professional colleagues, parents and the public; and from a belief that only some students can and need to achieve high learning standards to a conviction that all students need to meet such high standards.”
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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.147 |
| Scholarly communication | 0.031 | 0.047 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".