Bibliographic record
Abstract
Although research universities represent only fifteen to twenty per cent of national university systems worldwide, they provide the bulk of fundamental research and doctoral training. Written by two veteran university administrators, Leading Research Universities in a Competitive World focuses on the international ranking systems’ uneven distribution of these institutions in industrialized countries, and the organizational factors affecting their efficacy, prestige, and performance. Robert Lacroix and Louis Maheu argue that research universities, despite being embedded within academia’s mindset and rules, have to master market influences and relationships in order to produce new knowledge and attract the rare talent and limited financial assets required for successful research and education activities. Comparing the configuration of higher education systems in the US, UK, France, and Canada, the authors outline the ways in which research universities, which need public funding and have to engage diverse forms of state regulation, may possess sufficient autonomy to behave as independent actors. They demonstrate that reaching an equilibrium between autonomy and state regulation, though challenging, is an essential element in the success of high performing research universities. Leading Research Universities in a Competitive World illuminates the operation of these institutions through substantive quantitative and qualitative datasets to address the fundamental question of why universities perform differently.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".