Colleges as anchors of their communities: emergence and agglomeration
Bibliographic record
Abstract
This paper argues that colleges anchor their communities by developing knowledge and skills within their region; establishing links and exchanges outside the region; contributing to civic, political, cultural, and social activities and networks in their region; developing facilities and amenities used by the community; and, contributing to their region’s economic activity. Particularly important are colleges’ development of shared, collective or communal capacities, since these develop communities as communities, rather than individuals and organisations as discrete members of communities. We use Durkheimian sociology and critical realism to re-interpret the concept of agglomeration which we borrow from economics to trace the evolution of the concept of anchor institutions. Agglomeration is a particular type of emergence of phenomena from the interaction of components, where the whole is greater than the sum of its parts. We argue that colleges benefit their communities through the agglomeration of activities and organisations, from which emerge distinctive characteristics of their communities. This leads to the economic, social, and cultural benefits of agglomeration or clustering of activities within a geographic area. The accumulation of knowledge and knowledgeable people in communities increases the sharing of knowledge between people and organisations, and facilitates the generation and diffusion of knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".