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Record W1864953272 · doi:10.47678/cjhe.v33i3.183439

The Future of Merger What Do We Want Mergers To Do: Efficiency or Diversity?

2003· article· en· W1864953272 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Higher Education · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMerge (version control)Diversification (marketing strategy)Mergers and acquisitionsHigher educationBusinessPhenomenonEconomicsAccountingMarket economyMarketingPublic relationsFinancePolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.285
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it