The Future of Merger What Do We Want Mergers To Do: Efficiency or Diversity?
Bibliographic record
Abstract
Mergers have been a frequent phenomenon in higher education in the last quarter century. The conventional wisdom is that mergers are undertaken mainly for economic reasons, either to expand markets or to reduce costs. About four out of five college or university mergers survive. In the for-profit sector the comparable rate is closer to two out of five. From this one might conclude that the future for mergers among colleges and universities is robust. If, however, the principal purpose of mergers is economic efficiency, there logically ought to be a point beyond which the efficacy of merger will begin to decline. There is, however, another motive for merger, which is unrelated to economic efficiency. Mergers can produce greater diversity of programs and services, both among individual colleges and universities and within systems of postsecondary education. If diversification is the primary purpose of merger, the future might look different and might depend on new ways of identifying peers and partners for merger. This essay examines the expectations that are held for mergers, the realism of those expectations, and the means by which partners in mergers are identified and selected. It concludes with the suggestions that diversification may replace efficiency as the main stimulus of merger, and that, as the choice is made between efficiency and merger, institutions and systems of post- secondary education may try other, less permanent, forms of inter-institutional cooperation before committing to merge.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".