Building a Canadian Business Ethics Research Network - SKC Proposal
Bibliographic record
Abstract
Part I: "A Story of Two Journeys", outlines what has been accomplished with SSHRC funding in phase one, the concept paper phase, and phase two, the interim funding phase, set against SSHRC's assessment criteria. Part II: "Meeting the Objectives of the Strategic Knowledge Clusters Program", addresses a question: "Is it reasonable to expect that a fully functional, well managed, business ethics research network will meet the objectives set out in the work of everyone involved in the development of this proposal through phases one and two, as well as phase three, the preparation of this application. Part III: Describes in detail the activities planned for the network, the management and governance structure that will guide the implementation of our plans, the team of participants who will provide leadership, and our budget.
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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 | 0.082 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".