Triple Bottom Line Accounting and Energy-Efficiency Retrofits in the Social-Housing Sector: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT This paper reports the findings of a case study conducted to learn about the information, actors, actions, and processes involved in energy-efficiency investment decisions in the social-housing sector. These decisions draw on environmental, social, and economic factors, which are studied from a “triple bottom line” (TBL) accounting perspective. The quantitative methods we use rely on Levels I, II, and III fair-value measures similar to those used in financial accounting. The qualitative methods rely primarily on interviews conducted and transcribed by the researchers. Our main findings show that a pure financial bottom-line approach would not fully indicate the overall desirability of the type of energy-efficiency investment undertaken in this case. By factoring in other quantitative and qualitative outcomes drawn from the research methods applied, a different conclusion may be reached. Data Availability: Available upon request from the authors.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it