Supported by Fednor DRAFT Cluster Theory as Constructive Confusion: With applications to Sudbury
Bibliographic record
Abstract
The goal is an old one: find ways to promote the wealth of the nation. It was Smith’s goal in writing An Inquiry into the Nature and Causes of the Wealth of Nations, published in 1776. It was Harvard economist Michael Porter’s goal in 1990 when he published The Competitive Advantage of Nations. The two books had similar objectives, and surprisingly similar conclusions. Porter quickly became the prophet of what seemed to be a new gospel called the “cluster approach”, which he calls “a new way of thinking ” about economic development. Cluster theory has spread around the world and into some surprising corners. In Sudbury alone in the past year there have been eight Cluster events ( see Table 1). There have also been articles in the local newspaper (including one on the Sudbury Bar Cluster by ex Laurentian Student, Laura Stradiato, and a full page article by Stan Sudol, a Toronto Media consultant who grew up in Sudbury), several pieces on the INORD websites, and editorials promoting the cluster approach. On November 9, 2002 I gave a speech on the MS&S cluster to the annual meeting of the field staff of the Federal
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 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 | 0.000 | 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) | 0.013 | 0.004 |
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; both teacher heads agree on what is shown here.
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".