Bibliographic record
Abstract
Proposal There is rising concern that current approaches to environmental management systems are yielding little in the way of meaningful environmental performance improvement. No small concern because the scale of the investment in EMS is colossal, but often underestimated. Over 95% of the real economic cost of EMS is attributable to factors which are difficult to measure - time inputs from personnel working within organizations on program development (drafting and reviewing policies, procedures and the like), training (most personnel will be trained), auditing (internal and maybe 3rd party) and very especially the pre-audit blitz that precedes most audits by 3rd parties. Concerns about the value derived from implementing and certifying EMSs have not impacted growth in the field. Indeed there has been a significant uplift in the level of activity with the European Commission, EU Member State governments, US State and Federal government agencies joining with industry sector associations and others in the call to promote – and in many cases require - the uptake of management systems and third party certification of them. US regulators in the state of Texas and other US states are offering meaningful incentives to organisations who meet a defined set of EMS requirements The American Chemistry Council is requiring all of their members to implement certified EMSs by the end of 2006. The US EPA has included requirements for 3rd party approved EMS’s in sentencing guidelines. Those who are voicing concerns about the value of EMS point to an increasing body of anecdotal and empirical evidence which has found little or no correlation between certified EMSs and a variety of environmental performance metrics. The findings of these studies are surprising for many: the broad-based uptake of management systems in the environmental field was seen as a natural progression away from end-of-pipe thinking and most expected it to lead to significant operational efficiencies and other environmental performance gains.
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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".