How Can Accountants Enhance (or Save) Natural and Cultural Capital Valuation? Engaging Academics: A Collaboration with <scp>CPA</scp> Canada and the Canadian Commission for <scp>UNESCO</scp>*
Bibliographic record
Abstract
ABSTRACT Accountants should engage more with natural and cultural capital accounting to make tools more accessible and to ensure critical information is provided to decision‐makers. While ecological economists have continued to innovate and design tools, corporate‐level accounting has seemingly lagged behind. We argue that the time is now for accounting researchers and practitioners to come together to build upon these tools to advance a sustainable agenda. We examine possibilities through the novel lens of a recent Chartered Professional Accountants of Canada and Canadian Commission for United Nations Educational, Scientific and Cultural Organization (UNESCO) report that explores tools to measure natural and cultural capital at UNESCO designated sites in Canada. From here, we pose a variety of questions, critiques, and pathways for researchers and practitioners alike.
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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 | 0.079 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.049 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".