Doing Well by Doing Good: \nIs doing good, good for business?
Bibliographic record
Abstract
This study explores whether “doing good” is actually good for business, and examines how and when that impact is measured. Several research methods have been used to gather data to support this work: a literature review, expert interviews, case studies and subject matter interviews; all of these were conducted in Toronto. This study confirms that doing good is good for business and that doing good is measurable, while noting that the ways in which impact is measured may be outdated and lacking effectiveness. Corporations will likely increase and further be able to strategize their purpose-based efforts if they can better understand how much doing good affects their bottom lines. With correct research investment in order to prevent unintended consequences there is tremendous potential to do even more good. Findings have potential directly and indirectly to affect a spectrum of stakeholder groups including business owners, corporate leadership, employees and society. This study includes observations and recommendations to help more businesses do more good by using technology creatively to allow for accurate measurement and clearer insights, to tie CSR success directly to both the overall corporate strategy and to executive compensation to help ensure overall success.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".