Bibliographic record
Abstract
Purpose This paper seeks to present a partnering model pioneered in Sudbury, Canada, that combats fragmentation in the delivery of business support services. Partnerships form the backbone of a comprehensive business support centre. The Sudbury Regional Business Centre brings together banking, government, communications, education, legal, and accounting partners. These partners coordinate their efforts to provide services to area businesses, striving to improve the odds for a dynamic and resilient local economy. This model has been replicated and adapted by various communities across Ontario, Canada's most populous province. Design/methodology/approach The methodology consists of qualitative research by two participant‐observers. Findings If a solid business case can be made, banks and three levels of government can be persuaded to act in concert with other partners through a community business support centre; economic upheaval in the Sudbury area precipitated urgency and resolve; several postulates regarding key success factors were supported. Research limitations/implications The reporting is retrospective; the postulates are examined in a single context. Practical implications Disparate players can form a working partnership so that business support services from various quarters blend effectively, and are easily accessed to give clients the best help available. Originality/value This paper describes how a community forged partnerships designed to overcome fragmentation in the delivery of business support services. Comprehensive enterprise support offered by committed public and private sector partners can be channelled through a physically accessible centre. The factors which proved most critical to success are discussed.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".