THE BNQ21000 STANDARD: THE MANAGEMENT OF SUSTAINABLE DEVELOPMENT – FROM LEARNING TO AUDIT
Bibliographic record
Abstract
Issued in 2012, the learning Standard BNQ21000 is a non-auditable standard that focuses on the management of sustainable development (SD) within the management of manufacturing companies.This standard covers 21 corporate issues that are grouped under four themes: economic, social, environmental and moral.This frame of reference aims at guiding businesses towards the social project, as intended by the SD Act of Québec, which is based on the Rio principles.In this standard, each issue is classified according to five levels of maturity: 1. somewhat concerned; 2. reactive; 3. accommodating; 4. proactive; and 5. generative.The classified issues are thus integrated into a single table to form the self-assessment grid.The bases of good SD management begin at the 3rd level, while the inferior levels point at the most obvious gaps.This article unfolds in three parts.First, we outline the conceptualisation and learning mechanism of this standard.We explain how the principle of Sextant, which acts as the base to the self-assessment grid, enables to seize and gradually integrate the principles of SD.The second part shows the higher-level results from 40 pilot projects and conducts a review of the main developments and improvements to be made in the forthcoming standard reform.Finally, after 5 years of deployment, it was agreed to revise the Standard to extend the issues in order to cover other sectors besides manufacturing.In addition, an auditable version of the latter will be deployed.Work is set to begin in 2017.We will conclude with the discussion of various possible avenues for the overhaul.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".