Bibliographic record
Abstract
Keith MacMaster is a legal and financial policy researcher focusing on sustainable finance. His research is directed at two broad areas: climate finance and deep seabed mining. The first area aims at improving responsible investing and creating new sustainable financial products. The second area analyzes the financial mechanisms relating to deep seabed mining, ensuring parties responsible for environmental damage have liability attached.\nHis PhD thesis, "Comprehensive Wealth and the Development of Deep Seabed Mining Finance", addresses the financial and investment issues in deep seabed mining, arguing that additional measures are necessary to prevent environmental and social harms, to provide for compensation and remediation should those harms occur, and to ensure that the principles of the common heritage of mankind are implemented.\nCurrently teaching LAWS1015 – Property in its Historical Context, and COMM2603 – Legal Aspects of Business.\nDoctorate in Law (PhD) – Anticipated August 2021\n• Dalhousie University\n• Thesis: Comprehensive Wealth and the Development of Deep Seabed Mining Finance\nMaster of Law (LLM) – October 2018\n• Dalhousie University – Responsible Investing for Retail Consumers: Access Denied\n• Thesis available at: http://hdl.handle.net/10222/74170\nPersonal Financial Planner - 2015\n• Canadian Securities Institute - Toronto, ON\nMaster of Business Administration (MBA) - 2007\n• Richard Ivey School of Business, London, ON\nBachelor of Laws (LL.B.) - 2003\n• Dalhousie University - Halifax, Nova Scotia\nBachelor of Science (Distinction) (B.Sc.) - 2000\n• University College of Cape Breton – Sydney, NS
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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 | 0.000 | 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) | 0.050 | 0.037 |
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".